Methods of manufacturing and assessing orthodontic aligners

ABSTRACT

A method of manufacturing and analyzing a quality of an orthodontic aligner is described. An orthodontic aligner is manufactured by printing a mold associated with a dental arch of a patient based on a digital model of the mold, forming an orthodontic aligner over the mold, and trimming the orthodontic aligner. A quality of the orthodontic aligner is then assessed by imaging the orthodontic aligner to generate a first digital representation of the orthodontic aligner, comparing the first digital representation of the orthodontic aligner to a digital file associated with the orthodontic aligner, determining whether a cutline variation is detected between the orthodontic aligner and the digital file, and determining whether there is a manufacturing defect of the orthodontic aligner based on whether the cutline variation exceeds a cutline variation threshold.

RELATED APPLICATIONS

This patent application is a continuation application of U.S.application Ser. No. 16/851,038, filed Apr. 16, 2020, which is acontinuation application of U.S. application Ser. No. 16/435,322, filedJun. 7, 2019, which is a continuation application of U.S. applicationSer. No. 16/145,016, filed Sep. 27, 2018, which claims the benefit under35 U.S.C. § 119(e) of U.S. Provisional Application No. 62/609,723, filedDec. 22, 2017, and of U.S. Provisional Application No. 62/566,188, filedSep. 29, 2017, all of which are herein incorporated by reference intheir entirety. This patent application is related to U.S. applicationSer. No. 16/937,512, filed Jul. 23, 2020. This application is furtherrelated to U.S. application Ser. No. 17/220,807, filed Apr. 1, 2021.

TECHNICAL FIELD

Embodiments of the present invention relate to the field ofmanufacturing custom products and, in particular, to image based qualitycontrol systems and methods for custom manufactured products.

BACKGROUND

For some applications, shells are formed around molds to achieve anegative of the mold. The shells are then removed from the molds to befurther used for various applications. One example application in whicha shell is formed around a mold and then later used is correctivedentistry or orthodontic treatment. In such an application, the mold maybe a positive mold of a dental arch for a patient and the shell may bean aligner to be used for aligning one or more teeth of the patient.When attachments are used, the mold may also include features associatedwith planned orthodontic attachments and virtual fillers.

Molds may be formed using casting or rapid prototyping equipment. Forexample, 3D printers may manufacture the molds using additivemanufacturing techniques (e.g., stereolithography) or subtractivemanufacturing techniques (e.g., milling). The aligners may then beformed over the molds using thermoforming equipment. Once the aligner isformed, it may be manually or automatically trimmed. In some instances,a computer controlled 4-axis or 5-axis trimming machine (e.g., a lasertrimming machine or a mill) is used to trim the aligner along a cutline.The trimming machine uses electronic data that identifies the cutline totrim the aligner. Thereafter, the aligner may be removed from the moldand delivered to the patient. While much of this process has beenautomated, further improvements may be had.

SUMMARY

A first aspect of the disclosure may include a method includingreceiving a digital file associated with a plastic shell that iscustomized for a dental arch of a patient, generating a first image ofthe plastic shell using one or more imaging devices, determining aninspection recipe for the plastic shell based on at least one of firstinformation associated with the first image of the plastic shell orsecond information associated with the digital file. The inspectionrecipe specifies one or more additional images of the plastic shell tobe generated. The method may also include performing the inspectionrecipe to capture the one or more additional images of the plasticshell, determining whether one or more defects are included in theplastic shell based at least in part on the one or more additionalimages, and performing quality control for the plastic shell responsiveto determining that one or more defects are included in the plasticshell.

A second aspect of the disclosure may further extend the first aspect ofthe disclosure. In the second aspect of the disclosure, the first imageis a top view image of the plastic shell, and determining the inspectionrecipe includes determining one or more features of the plastic shellusing at least one of the first information or the second informationand determining the one or more additional images to be generated basedon the one or more features. The one or more features include at leastone of a precision cutline of the plastic aligner, a cavity of theplastic aligner associated with an attachment, an angle of a cutline ofthe plastic aligner, a distance between cavities of the plastic alignerassociated with teeth, or a distance between cavities of the plasticaligner associated with attachments. Determining the inspection recipemay also include determining a size of the plastic shell from at leastone of the first information or the second information and determiningsettings to generate the one or more additional images based on at leastone of the one or more features or the size of the plastic shell. Thesettings may include at least one of an orientation of the one or moreimaging devices, a zoom of the one or more imaging devices, or a focusof the one or more imaging devices. Performing the inspection recipe mayinclude capturing the one or more additional images using the settings.

A third aspect of the disclosure may further extend the first and/orsecond aspects of the disclosure. The third aspect of the disclosure mayinclude applying the digital file to a model as an input, generating, bythe model, an output identifying one or more locations of the plasticshell that are identified as high risk areas for the one or moredefects, and determining the one or more additional images based on theone or more locations identified as the high risk areas by the output.

A fourth aspect of the disclosure may further extend the first throughthird aspects of the disclosure. The fourth aspect of the disclosure mayinclude applying the digital file to a trained machine learning model asan input. The trained machine learning model is trained to identify oneor more high risk areas for the one or more defects at one or morelocations of the plastic shell. The fourth aspect may also includegenerating, by the trained machine learning model, an output identifyingthe one or more locations of the plastic shell that are identified asthe high risk areas for the one or more defects, and determining the oneor more additional images based on the one or more locations identifiedas the high risk areas by the output.

A fifth aspect of the disclosure may further extend the first throughfourth aspects of the disclosure. The fifth aspect of the disclosure mayinclude applying the digital file to a predictive model as an input. Thepredictive model performs finite element analysis using a geometry ofthe plastic shell to calculate one or more values of one or more strainsof the plastic shell and to identify one or more high risk areas for theone or more defects at one or more locations of the plastic shell basedon the one or more values of the one or more strains exceeding athreshold at the one or more locations. The fifth aspect may alsoinclude generating, by the predictive model, an output identifying theone or more locations of the plastic shell that are identified as thehigh risk areas for the one or more defects, and determining the one ormore additional images based on the one or more locations identified asthe high risk areas by the output.

A sixth aspect of the disclosure may further extend the first throughfifth aspects of the disclosure. The sixth aspect of the disclosure mayinclude determining the inspection recipe including applying the digitalfile to a rules engine that uses one or more rules that specifygenerating the one or more additional images of the plastic shell whenone or more features are included in the plastic shell at a location ofthe plastic shell, and performing the inspection recipe includescapturing the one or more additional images.

A seventh aspect of the disclosure may further extend the sixth aspectof the disclosure. The seventh aspect of the disclosure may includegenerating the one or more rules based on at least one of a) historicaldata including reported defects of a first set of plastic shells andlocations of the reported defects on the first set of plastic shells, b)digital files of a second set of plastic shells with labels indicatingwhether or not each of the second set of plastic shells experienced adefect, or c) digital files of a third set of plastic shells with labelsindicating whether or not each of the third set of plastic shellsinclude a probability that a defect is present in the plastic shell.

An eighth aspect of the disclosure may further extend the first throughseventh aspects of the disclosure. In the eighth aspect of thedisclosure, determining the inspection recipe includes retrieving theinspection recipe from a memory location. Settings for the one or moreimaging devices to generate the one or more additional images are presetin the inspection recipe retrieved from the memory location. Thesettings include at least one of one or more locations for the one ormore imaging devices to generate the one or more additional images, oneor more orientations for the one or more imaging devices to capture theone or more additional images, one or more depths of focus for the oneor more imaging devices to capture the one or more additional images, ora number of the one or more additional images for the one or moreimaging devices.

A ninth aspect of the disclosure may further extend the first througheighth aspects of the disclosure. In the ninth aspect of the disclosure,performing the inspection recipe to capture the one or more additionalimages further includes tracing, using a first imaging device of the oneor more imaging devices, an edge of the plastic shell using data fromthe design file of the plastic shell to capture a subset of images ofthe one or more additional images that represent a cutline of theplastic shell.

A tenth aspect of the disclosure may further extend the first throughninth aspects of the disclosure. In the tenth aspect of the disclosure,determining whether the one or more defects are included in the plasticshell based at least in part on the one or more additional imagesfurther includes obtaining a digital model of the plastic shell from thedigital file associated with the plastic shell, determining anapproximated first property for the plastic shell from the digital modelof the plastic shell, determining a second property of the plastic shellfrom the first image, and comparing the approximated first property tothe second property.

An eleventh aspect of the disclosure may further extend the firstthrough tenth aspects of the disclosure. The eleventh aspect of thedisclosure may include performing the inspection recipe to capture theone or more additional images of the plastic shell including configuringsettings of the one or more imaging devices to capture the one or moreimages based at least on one of the first information or the secondinformation. The settings include at least one of an orientation of theone or more imaging devices, a location of the one or more imagingdevices, a zoom of the one or more imaging devices, or a depth of focusof the one or more imaging devices.

A twelfth aspect of the disclosure may further extend the first througheleventh aspects of the disclosure. In the twelfth aspect of thedisclosure, the first image of the plastic shell includes at least oneof a photographic image, an X-ray image, or a digital image, and the oneor more imaging devices includes at least one of a camera, a blue laserscanner, a confocal microscope, a stereo image sensor, an X-ray device,or an ultrasonic device.

A thirteenth aspect of the disclosure may include a method includingobtaining one or more images of a first shell that is customized for adental arch of a patient, identifying an identifier on the first shellusing the one or more images, and determining, from a set of digitalfiles, a first digital file associated with the first shell based on theidentifier, determining an approximated first property for the firstshell from the first digital file. The approximated first property isbased on a manipulation of a digital model of a mold used to create thefirst shell. The method also includes determining a second property ofthe first shell from the one or more images, comparing the approximatedfirst property to the second property, and performing quality controlfor the first shell based on the comparing.

A fourteenth aspect of the disclosure may extend the thirteenth aspectof the disclosure. In the fourteenth aspect of the disclosure, thedigital file includes a digital model of the first shell. Additionally,the method also includes generating the digital model of the first shellby performing the following including: simulating a process ofthermoforming a film over a digital model of the mold by enlarging thedigital model of the mold into an enlarged digital model, computing aprojection of a cutline onto the enlarged digital model, virtuallycutting the enlarged digital model along the cutline to create a cutenlarged digital model, and selecting an outer surface of the cutenlarged digital model.

A fifteenth aspect of the disclosure may extend the thirteenth and/orfourteenth aspects of the disclosure. In the fifteenth aspect of thedisclosure, the digital file includes the digital model of the mold usedto create the first shell. Additionally, the method also includesmanipulating the digital model of the mold to determine the approximatedfirst property.

A sixteenth aspect of the disclosure may extend the thirteenth throughfifteenth aspects of the disclosure. In the sixteenth aspect of thedisclosure, the approximated first property includes an approximatedouter surface of the first shell, and the second property includes afirst shape of the first shell.

A seventeenth aspect of the disclosure may extend the sixteenth aspectof the disclosure. In the seventeenth aspect of the disclosure, the oneor more images include a top view image. Additionally, the method alsoincludes determining a first plane associated with the top view image,computing a first projection of the approximated outer surface of thefirst shell into the first plane, identifying, based on the comparing,one or more differences between a second shape of the first projectionand the first shape, and determining whether the one or more differencesexceed a first threshold.

An eighteenth aspect of the disclosure may extend the seventeenth aspectof the disclosure. In the eighteenth aspect of the disclosure the methodalso includes determining whether the first shell is deformed based onwhether the difference exceeds the first threshold.

A nineteenth aspect of the disclosure may extend the seventeenth througheighteenth aspects of the disclosure. In the nineteenth aspect of thedisclosure, identifying the one or more differences includes determiningone or more regions where the first shape and the second shape do notmatch, and determining at least one of a thickness of the one or moreregions or an area of the one or more regions.

A twentieth aspect of the disclosure may extend the seventeenth throughnineteenth aspects of the disclosure. In the twentieth aspect of thedisclosure, the digital file includes a first digital model of the firstshell that includes the approximated first property, the first digitalmodel of the first shell having been generated based on the manipulationof the digital model of the mold. The method also includes determining aresting position of the first digital model for the first shell on aflat surface, and computing a projection of the first digital modelhaving the resting position onto the first plane of the top view image.

A 21^(st) aspect of the disclosure may extend the 20^(th) aspect of thedisclosure. In the 21^(st) aspect of the disclosure, determining theresting position of the first digital model on the flat surface includesdetermining a center of mass of the first digital model of the firstshell, determining a convex hull of the first digital model thatincludes a set of vertices linking outer most points of the firstdigital model, for at least one vertex of the set of vertices, andperforming the following including: computing a line containing the atleast one vertex, computing a projection of the center of mass onto apoint on the line, determining whether the point on the line liesoutside of the at least one vertex, and responsive to determining thatthe point does not lay outside the at least one vertex, determining thatthe at least one vertex is the resting position for the first digitalmodel.

A 22^(nd) aspect of the disclosure may extend the 20^(th) through21^(st) aspects of the disclosure. In the 22^(nd) aspect of thedisclosure, determining the resting position of the first digital modelon the flat surface includes determining a center of mass of the firstdigital model of the first shell, determining a convex hull of the firstdigital model that includes a set of faces linking outer most points ofthe first digital model, and for at least one face of the set of faces,performing the following including: computing a plane containing the atleast one face, computing a projection of the center of mass onto apoint on the plane, determining whether the point on the plane liesoutside of the at least one face, and responsive to determining that thepoint does not lay outside the at least one face, determining that theat least one face is the resting position for the first shell.

A 23^(rd) aspect of the disclosure may extend the 17^(th) through22^(nd) aspects of the disclosure. In the 23^(rd) aspect of thedisclosure, the method includes determining that the one or moredifferences do not exceed the first threshold, generating a modifiedprojection of the approximated outer surface of the first shell bydeforming the second shape of the first projection to cause a firstcurvature of a deformed second shape to approximately match a secondcurvature of the first shape, identifying one or more additionaldifferences between the first curvature of the deformed second shape andthe second curvature of the first shape, and determining whether the oneor more additional differences exceed a second threshold.

A 24^(th) aspect of the disclosure may extend the 23^(rd) aspect of thedisclosure. In the 24^(th) aspect of the disclosure, the method includesidentifying one or more regions where the second curvature and the firstcurvature do not match. The one or more regions correspond to a cutlineof the first shell. The method also includes determining whether thecutline of the first shell will interfere with a fit of the first shellon the dental arch of the patient.

A 25^(th) aspect of the disclosure may extend the 23^(rd) or 24^(th)aspects of the disclosure. In the 25^(th) aspect of the disclosure,deforming the first shape of the first projection includes identifying amiddle line of the first projection, computing a projection of a set oflines that perpendicularly intersect the middle line, identifying pointson the set of lines at an intersection between each respective line ofthe set of lines and the middle line, and moving the points along theset of lines to cause the first curvature of the deformed second shapeto approximately match the second curvature of the first shape.

A 26^(th) aspect of the disclosure may extend the 17^(th) through25^(th) aspects of the disclosure. In the 26^(th) aspect of thedisclosure, the one or more images further include a side view image.Additionally, the method also includes generating a modified projectionof the approximated outer surface of the first shell by deforming thesecond shape of the first projection to cause a first curvature of adeformed second shape to approximately match a second curvature of thefirst shape, determining a second plane associated with the side viewimage, deforming the approximated outer surface of the first shell inaccordance with the deforming of the second shape of the firstprojection, computing a second projection of the deformed approximatedouter surface of the first shell onto the second plane, determining oneor more additional differences between a third shape of the first shellas represented in the side view image and a fourth shape as representedin the second projection, and determining whether the one or moreadditional differences exceed a second threshold.

A 27^(th) aspect of the disclosure may include a non-transitory,computer-readable medium storing instructions that, when executed by aprocessing device, cause the processing device to obtain one or moreimages of a first shell that is customized for a dental arch of apatient, identify an identifier on the first shell using the one or moreimages, determine, from a set of digital files, a first digital fileassociated with the first shell based on the identifier, determine anapproximated first property for the first shell from the first digitalfile, wherein the approximated first property is based on a manipulationof a digital model of a mold used to create the first shell, determine asecond property of the first shell from the one or more images, comparethe approximated first property to the second property, and performquality control for the first shell based on the comparing.

A 28^(th) aspect of the disclosure may further extend the 27^(th) aspectof the disclosure. In the 28^(th) aspect of the disclosure, the digitalfile includes a digital model of the first shell. Additionally, theprocessing device is further to generate the digital model of the firstshell by performing the following including: simulate a process ofthermoforming a film over a digital model of the mold by enlarging thedigital model of the mold into an enlarged digital model, compute aprojection of a cutline onto the enlarged digital model, virtually cutthe enlarged digital model along the cutline to create a cut enlargeddigital model, and select an outer surface of the cut enlarged digitalmodel.

A 29^(th) aspect of the disclosure may further extend the 28^(th) aspectof the disclosure. In the 29^(th) aspect of the disclosure, theapproximated first property includes an approximated outer surface ofthe first shell, and the second property includes a shape of the firstshell.

A 30^(th) aspect of the disclosure may include a system, including amemory storing instructions, and a processing device coupled to thememory. Executing the instructions causes the processing device toobtain one or more images of a first shell that is customized for adental arch of a patient, identify an identifier on the first shellusing the one or more images, determine, from a set of digital files, afirst digital file associated with the first shell based on theidentifier, determine an approximated first property for the first shellfrom the first digital file, wherein the approximated first property isbased on a manipulation of a digital model of a mold used to create thefirst shell, determine a second property of the first shell from the oneor more images, compare the approximated first property to the secondproperty, and perform quality control for the first shell based on thecomparing.

A 31^(st) aspect of the disclosure may further extend the 30^(th) aspectof the disclosure. In the 31^(st) aspect of the disclosure, the one ormore images include a top view image, the approximated first propertyincludes an approximated outer surface of the first shell and the secondproperty includes a first shape of the first shell. Additionally, theprocessing device is further to determine a first plane associated withthe top view image, compute a first projection of the approximated outersurface of the first shell into the first plane, identify, based on thecomparing, one or more differences between the second shape of the firstprojection and the first shape of the first shell, and determine whetherthe one or more differences exceed a first threshold.

A 32^(nd) aspect of the disclosure may further extend the 31^(st) aspectof the disclosure. In the 32^(nd) aspect of the disclosure, theprocessing device is further to determine that the one or moredifferences do not exceed the first threshold, generate a modifiedprojection of the approximated outer surface of the first shell bydeforming the second shape of the first projection to cause a firstcurvature of a deformed second shape to approximately match a secondcurvature of the first shape, identify one or more additionaldifferences between the first curvature of the deformed second shape andthe second curvature of the first shape, and determine whether the oneor more additional differences exceed a second threshold.

A 33^(rd) aspect of the disclosure may include a method for inspecting adental appliance for manufacturing defects. The method includesobtaining one or more images of the dental appliance, identifying anidentifier of the dental appliance, determining, from a set of digitalfiles, a digital file associated with the dental appliance based on theidentified identifier, the digital file associated with the dentalappliance including a digital model of an intermediate component usedduring manufacture of the dental appliance, determining a intendedproperty for the dental appliance by digitally manipulating the digitalmodel of the intermediate component used during manufacture of thedental appliance, determining an actual property of the dental appliancefrom the one or more images of the dental appliance, determining whetherthere is a manufacturing defect in the dental appliance by comparing theintended property for the dental appliance with the actual property ofthe dental appliance, and outputting an output associated with thedetermination of whether there is a manufacturing defect.

A 34^(th) aspect of the disclosure may further extend the 33^(rd) aspectof the disclosure. In the 34^(th) aspect of the disclosure, the dentalappliance includes a customized orthodontic aligner customized for aspecific arch of a specific patient and a specific stage of orthodontictreatment, and the intermediate component includes a positive moldassociated with the specific arch of the patient and the specific stageof orthodontic treatment.

A 35^(th) aspect of the disclosure may further extend the 33^(rd) or34^(th) aspects of the disclosure. In the 36^(th) aspect of thedisclosure, the intended property for the customized orthodontic alignerincludes an intended silhouette of the customized orthodontic aligner ina plane and the actual property of the customized orthodontic alignerincluding the actual silhouette of the customized orthodontic aligner inthe plane as captured by one or more images of the customizedorthodontic aligner.

A 36^(th) aspect of the disclosure may further extend the 35^(th)aspects of the disclosure. The 36^(th) aspect of the disclosure mayinclude comparing the intended property with the actual property of thecustomized orthodontic aligner including comparing the intendedsilhouette with the actual silhouette and determining whetherdifferences exceed a threshold value.

A 37^(th) aspect of the disclosure may further extend the 34^(th)through 36^(th) aspects of the disclosure. In the 37^(th) aspect of thedisclosure, the intended property for the customized orthodontic alignerincludes an intended cutline for the customized orthodontic aligner. Theactual property of the customized orthodontic aligner includes theactual cutline of the customized orthodontic aligner as determined fromthe one or more images.

A 38^(th) aspect of the disclosure may further extend the 34^(th)through 37^(th) aspects of the disclosure. In the 38^(th) aspect of thedisclosure, the identifier of the customized orthodontic aligner isprinted on the customized orthodontic aligner and the identifier of thecustomized orthodontic aligner is identified by analyzing the one ormore images of the customized orthodontic aligner.

A 39^(th) aspect of the disclosure may further extend the 34^(th)through 38^(th) aspects of the disclosure. In the 39^(th) aspect of thedisclosure, identifying the identifier of the customized orthodonticaligner includes receiving user input of the identifier.

A 40^(th) aspect of the disclosure may further extend the 34^(th)through 39^(th) aspects of the disclosure. In the 40^(th) aspect of thedisclosure, the method also includes determining an inspection recipefor the customized orthodontic aligner based on the obtained one or moreimages of the customized orthodontic aligner or the digital fileassociated with the customized orthodontic aligner.

A 41^(st) aspect of the disclosure may further extend the 40^(th) aspectof the disclosure. In the 41^(st) aspect of the disclosure, theinspection recipe specifies one or more additional images of thecustomized orthodontic aligner to be captured.

A 42^(nd) aspect of the disclosure may further extend the 40^(th)through 41^(st) aspects of the disclosure. In the 42^(nd) aspect of thedisclosure, the inspection recipe is based on the digital fileassociated with the customized orthodontic aligner, and the inspectionrecipe is determined by a predictive model that identifies locations ofthe customized orthodontic aligner that are at higher risk for one ormore defects.

A 43^(rd) aspect of the disclosure may further extend the 34^(th)through 42^(nd) aspects of the disclosure. In the 43^(rd) aspect of thedisclosure, the intended property for the customized orthodontic aligneris determined by digitally manipulating a portion of a surface of thedigital model of the mold to approximate a surface of the customizedorthodontic aligner.

A 44^(th) aspect of the disclosure may further extend the 43^(rd) aspectof the disclosure. In the 44^(th) aspect of the disclosure, the surfaceof the customized orthodontic aligner is approximated by offsetting theportion of the surface of the digital model of the mold by a distance.

A 45^(th) aspect of the disclosure may further extend the 43^(rd)through 44^(th) aspects of the disclosure. In the 45^(th) aspect of thedisclosure, the intended property for the customized orthodontic aligneris determined by virtually projecting a cut line associated with thecustomized orthodontic aligner to the approximated surface of thecustomized orthodontic aligner.

A 46^(th) aspect of the disclosure may further extend the 43^(rd)through 45^(th) aspects of the disclosure. In the 46^(th) aspect of thedisclosure, the intended property for the customized orthodontic alignerincludes an intended silhouette of the customized orthodontic aligner ina plane and the intended silhouette of the customized orthodonticaligner is determined by computing a silhouette of the approximatedsurface of the customized orthodontic aligner in the plane.

A 47^(th) aspect of the disclosure may further extend the 34^(th)through 46^(th) aspects of the disclosure. In the 47^(th) aspect of thedisclosure, the intended property for the customized orthodontic aligneris determined by computing a silhouette of the digital model of the moldin a plane.

A 48^(th) aspect of the disclosure may further extend the 47^(th) aspectof the disclosure. In the 48^(th) aspect of the disclosure, the intendedproperty of the customized orthodontic aligner includes an intendedsilhouette of the customized orthodontic aligner in the plane, and theintended silhouette of the customized orthodontic aligner is computed byoffsetting a perimeter of the computed silhouette of the digital modelof the mold in the plane.

A 49^(th) aspect of the disclosure may further extend the 33^(rd)through 48^(th) aspects of the disclosure. In the 49^(th) aspect of thedisclosure, the dental appliance includes a mandibular advancementfeature.

A 50^(th) aspect of the disclosure may further extend the 33^(rd)through 49^(th) aspects of the disclosure. In the 50^(th) aspect of thedisclosure, the output includes a determination that a defect ispresent, and the output includes proposed digital modifications to thedigital model of the intermediate component used during manufacture ofthe dental appliance to limit future defects.

A 51^(st) aspect of the disclosure may further extend the 50^(th) aspectof the disclosure. In the 51^(st) aspect of the disclosure, the proposeddigital modifications to the digital model of the intermediate componentinclude at least one of added virtual filler material, revisions to acutline, and modifications to one or more attachments of theintermediate component.

A 52^(nd) aspect of the disclosure may include a method for inspecting adental appliance for manufacturing defects, the dental applianceconfigured for application to a dental arch of a patient, the methodincluding obtaining one or more images of the dental appliance,identifying an identifier of the dental appliance, determining, from aset of digital files, a digital file associated with the dentalappliance based on the identified identifier, the digital fileassociated with the dental appliance including a digital model of thedental appliance, the digital model of the dental appliance produced bydigitally manipulating a digital model of a dental arch of the patient,determining a intended property for the dental appliance from thedigital model of the dental appliance, determining an actual property ofthe dental appliance from the one or more images, determining whetherthere is a manufacturing defect in the dental appliance by comparing theintended property for the dental appliance with the actual property ofthe dental appliance, and outputting an output associated with thedetermination of whether there is a manufacturing defect.

A 53^(rd) aspect of the disclosure may further extend the 52^(nd) aspectof the disclosure. In the 53^(rd) aspect of the disclosure, the dentalappliance includes a customized orthodontic aligner customized for aspecific arch of a specific patient and a specific stage of orthodontictreatment, and the digital model of the customized orthodontic aligneris produced by manipulating a digital model of a staged dental arch ofthe patient that is associated with the specific stage of orthodontictreatment.

A 54^(th) aspect of the disclosure may further extend the 53^(rd) aspectof the disclosure. In the 54^(th) aspect of the disclosure, the intendedproperty for the customized orthodontic aligner includes an intendedsilhouette of the customized orthodontic aligner in a plane and theactual property of the customized orthodontic aligner includes theactual silhouette of the customized orthodontic aligner in the plane ascaptured by one or more images of the customized orthodontic aligner.

A 55^(th) aspect of the disclosure may further extend the 53^(rd) and/or54^(th) aspect of the disclosure. In the 55^(th) aspect of thedisclosure, the intended property for the customized orthodontic alignerincludes an intended cutline for the customized orthodontic aligner,wherein the actual property of the customized orthodontic alignerincludes the actual cutline of the customized orthodontic aligner asdetermined from the one or more images.

A 56^(th) aspect of the disclosure may further extend the 53^(rd)through 55^(th) aspects of the disclosure. In the 56^(th) aspect of thedisclosure, the method also includes determining an inspection recipefor the customized orthodontic aligner based on the obtained one or moreimages of the customized orthodontic aligner or the digital fileassociated with the customized orthodontic aligner.

A 57^(th) aspect of the disclosure may further extend the 53^(rd)through 56^(th) aspects of the disclosure. In the 57^(th) aspect of thedisclosure, the identifier of the customized orthodontic aligner isprinted on the customized orthodontic aligner and wherein the identifierof the customized orthodontic aligner is identified by analyzing the oneor more images of the customized orthodontic aligner or by receivinguser input of the identifier.

A 58^(th) aspect of the disclosure may further extend the 52^(nd)through 57^(th) aspects of the disclosure. In the 58^(th) aspect of thedisclosure, the dental appliance includes a removable palatal expander.

A 59^(th) aspect of the disclosure may further extend the 52^(nd)through 58^(th) aspects of the disclosure. In the 59^(th) aspect of thedisclosure, the dental appliance includes a removable surgical fixationdevice.

A 60^(th) aspect of the disclosure may further extend the 52^(nd)through 59^(th) aspects of the disclosure. In the 60^(th) aspect of thedisclosure, the dental appliance includes a removable mandibularadvancement feature.

A 61^(st) aspect of the disclosure may include a method for inspecting acustomized orthodontic aligner for manufacturing defects, the customizedorthodontic aligner customized for a specific arch of a specific patientand a specific stage of orthodontic treatment, the method includingobtaining one or more images of the customized orthodontic aligner,identifying an identifier of the customized orthodontic aligner,determining, based on the identifier of the customized orthodonticaligner, a intended property for the customized orthodontic aligner, theintended property for the customized orthodontic aligner determined bydigitally manipulating a digital model of a mold used during manufactureof the customized orthodontic aligner, determining an actual property ofthe customized orthodontic aligner from the one or more images,determining whether there is a manufacturing defect in the customizedorthodontic aligner by comparing the intended property for thecustomized orthodontic aligner with the actual property of thecustomized orthodontic aligner, and outputting an output associated withthe determination of whether there is a manufacturing defect.

A 62^(nd) aspect of the disclosure may further extend the 61^(st) aspectof the disclosure. In the 62^(nd) aspect of the disclosure, the intendedproperty for the customized orthodontic aligner includes an intendedsilhouette of the customized orthodontic aligner in a plane and theactual property of the customized orthodontic aligner includes theactual silhouette of the customized orthodontic aligner in the plane ascaptured by one or more images of the customized orthodontic aligner.

A 63^(rd) aspect of the disclosure may further extend the 61^(st)through 62^(nd) aspects of the disclosure. In the 63^(rd) aspect of thedisclosure, the intended property for the customized orthodontic alignerincludes an intended cutline for the customized orthodontic aligner,wherein the actual property of the customized orthodontic alignerincludes the actual cutline of the customized orthodontic aligner asdetermined from the one or more images.

A 64^(th) aspect of the disclosure may further extend the 61^(st)through 63^(rd) aspects of the disclosure. In the 64^(th) aspect of thedisclosure, the method also includes determining an inspection recipefor the customized orthodontic aligner based on the obtained one or moreimages of the customized orthodontic aligner or the digital fileassociated with the customized orthodontic aligner.

A 64^(th) aspect of the disclosure may further extend the 61^(st)through 64^(th) aspects of the disclosure. In the 64^(th) aspect of thedisclosure, the identifier of the customized orthodontic aligner isprinted on the customized orthodontic aligner and wherein the identifierof the customized orthodontic aligner is identified by analyzing the oneor more images of the customized orthodontic aligner or by receivinguser input of the identifier.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings.

FIGS. 1A-1B illustrate flow diagrams for methods of performing imagebased quality control for a shell using an inspection recipe, inaccordance with embodiments.

FIG. 2 illustrates an example imaging system including a top view cameraand a side view camera, in accordance with one embodiment.

FIGS. 3A-3B illustrate an example top view image and an example motioncontrol and screen path generated based on the top view image, inaccordance with one embodiment.

FIGS. 4A-4C illustrate an example side view composite image of a side ofthe shell, an edge detected using the side view composite image, and acomparison of the edge to a second edge obtained from a digital model ofthe shell, in accordance with one embodiment.

FIG. 5 illustrates a flow diagram for a method of determining aninspection recipe based on features of the plastic shell, in accordancewith one embodiment.

FIG. 6 illustrates a flow diagram for a method of determining one ormore additional images to generate based on output from a model, inaccordance with one embodiment.

FIG. 7 illustrates a flow diagram for a method of determining theinspection recipe using a rules engine, in accordance with oneembodiment.

FIGS. 8A-8B illustrate flow diagrams for methods of performing imagebased quality control for a shell, in accordance with embodiments.

FIGS. 9A-9B illustrate flow diagrams for methods of determining whethera shape of the shell is deformed, in accordance with embodiments.

FIG. 10 illustrates a digital model of an aligner projected onto animage of the aligner, in accordance with one embodiment.

FIG. 11 illustrates a flow diagram for a method of determining adifference in shape between the digital model of the shell and the imageof the shell, in accordance with one embodiment.

FIG. 12 illustrates a user interface used for image based qualitycontrol for a shell, in accordance with one embodiment.

FIGS. 13A-13B illustrate example comparisons of a contour of a digitalmodel of an aligner with a contour of an image of the aligner to detectdeformation, in accordance with one embodiment.

FIGS. 14A-14C illustrate flow diagrams for methods of deforming adigital model contour to more closely match the contour of the image ofthe aligner to detect cutline variations, in accordance with oneembodiment.

FIGS. 15A-15C illustrate examples of deforming a digital model contourto more closely match the contour of the image of the aligner to detectcutline variations, in accordance with one embodiment.

FIG. 16 illustrates a flow diagram for a method of generating a digitalmodel of the shell, in accordance with one embodiment.

FIG. 17 illustrates a flow diagram for a general method of determining aresting position of the digital model of the shell on a flat surface, inaccordance with one embodiment.

FIG. 18 illustrates a flow diagram for a method of determining a restingposition of the digital model of the shell on a flat surface using atwo-dimensional digital model, in accordance with one embodiment.

FIGS. 19A-19C illustrate example images for determining a restingposition of aligner on a flat surface, in accordance with oneembodiment.

FIG. 20 illustrates a flow diagram for a method of determining a restingposition of the digital model of the shell on a flat surface using athree-dimensional digital model, in accordance with one embodiment.

FIG. 21 illustrates a block diagram of an example computing device, inaccordance with one embodiment.

FIG. 22A illustrates an example side view image captured without abacking screen and without structured light illumination.

FIG. 22B illustrates an example side view image captured without abacking screen using structured light illumination (e.g., a focusedlight), in accordance with one embodiment.

FIG. 23 illustrates an example of crack detection in the contour of animage of an aligner captured using a focused light, in accordance withone embodiment.

DETAILED DESCRIPTION

Described herein are embodiments covering systems, methods, and/orcomputer-readable media suitable for image based quality control (IBQC)of custom manufactured products. The custom manufactured products may becustomized medical devices. For example, in some embodiments, the imagebased quality control systems and methods may be implemented in theinspection of orthodontic aligners after manufacturing. Quality controlof custom manufactured products is particularly difficult, especially inorthodontic aligner manufacturing where orthodontic aligners must beindividually customized for every single patient. Additionally, eachaligner in the series of aligners used to treat a single patient isunique compared to other aligners in the same series because eachaligner is specific to different stages of treatment. Furthercompounding the issue is that each patient receives a pair of alignersfor each stage of treatment, one unique aligner for treating the upperarch and one unique aligner for treating the lower arch. In someinstances, a single treatment can include 50-60 stages for treating acomplex case, meaning 100-120 uniquely manufactured aligners for asingle patient. When manufacturing aligners for patients worldwide, themanufacturing of several hundred thousand completely unique andcustomized aligners per day may be needed. As such, quality control ofthe custom manufactured products can be a particularly daunting task.Quality control of manufactured aligners may be performed to ensure thatthe aligners are defect free or that defects are within tolerablethresholds. The quality control process may be aimed at detecting one ormore of the following quality issues: arch variation, bend, cutlinevariation, debris, webbing, trimmed attachment, missing attachment, andso forth. Typically, a technician manually performs a quality controlprocess to inspect the aligners. However, this manual quality controlprocess may be very time consuming and prone to error due to theinherent subjectivity of the technician. As such, embodiments of thepresent invention may provide a more scalable, automated, and/orobjective aligner quality control process.

It should be noted that “aligner” and “shell” may be usedinterchangeably herein. As mentioned above, the embodiments may detectvarious quality issues for a given set of aligners. The quality issuesmay include one or more of arch variation, deformation, bend (compressedor expanded) of aligners, cutline variations, debris, webbing, trimmedattachments, missing attachments, burr, flaring, power ridge issues,material break, short hooks, bubbles, and so forth. Detecting thequality issues may enable fixing the aligner to remove the qualityissue, preventing the shipment of a malformed or subpar aligner, and/orremanufacture of the malformed aligner prior to shipment. In someembodiments, the identification of aligner quality issues may be basedon an image of an aligner compared with a digitally generated model ofeach of the aligner. In some embodiments, the digital model for eachaligner may be included in a digital file associated with the aligner.Optionally, the digital file associated with the manufactured alignermay provide a digitally approximated property (e.g., an outer surface ofthe aligner, a two-dimensional projection of the outer surface onto aplane, etc.) of the manufactured aligner. The digitally generated modelof the aligner and/or the digitally approximated property of the alignermay be based on a manipulation of a digital model of a mold used tocreate the aligner. In some embodiments, the identification of thealigner quality issues may be based on comparison of an approximatedfirst property of the aligner (as determined based on manipulation of adigital model of a mold used to manufacture the aligner) and adetermined second property of the aligner (as determined from one ormore image of the aligner). Some advantages of the disclosed embodimentsmay include automated detection of various aligner quality issues andautomated collection of data for statistical analysis. The embodimentsmay also improve detection results by eliminating human errors (e.g.,false positives and false negatives). Further, the embodiments mayreduce the amount of time it takes to perform quality control, therebydecreasing lead-time of aligners that may enable distributing thealigners to customers as scheduled.

Various software and/or hardware components may be used to implement thedisclosed embodiments. For example, software components may includecomputer instructions stored in a tangible, non-transitorycomputer-readable media that are executed by one or more processingdevices to perform image based quality control on customizedmanufactured shells (e.g., aligners). The software may setup andcalibrate cameras included in the hardware components, capture images ofaligners from various angles using the cameras, generate a digital modelfor the aligners, perform analysis that compares the digital model of analigner with the image of the aligner to detect one or more qualityissues (e.g., deformation, cutline variation, etc.), and classifyaligners based on results of the analysis.

In some implementations, a digital file associated with a shell that iscustomized for a dental arch of patient and undergoing inspection may bereceived. In some embodiments, the dental appliance includes identifyinginformation, such as a custom barcode or part identification number. Afirst image (e.g., photographic image, X-ray image, digital image) ofthe plastic shell may be generated using one or more imaging devices(e.g., a camera, a blue laser scanner, a confocal microscope, a stereoimage sensor, an X-ray device, an ultrasonic device, etc.). The dentalappliance identification information may be captured in the first imageand interpreted by the inspection system. Optionally, a technician mayalso manually input this information at an inspection station so thatthe inspection system can retrieve the digital file. In still furtherembodiments, a dental appliance sorting system may sort a series ofdental appliances in a known order. The inspection system may retrievethe dental appliance order from the dental appliance sorting system inorder to know which of the dental appliances are currently beinginspected and the order in which they arrive at the station. Optionally,the dental appliance may arrive at the inspection system in a traycarrying the dental appliance identifying information (e.g., RFID tag,barcode, serial numbers, etc.), which is read by the inspection system.Thereafter, the inspection system may retrieve the digital fileassociated with the dental appliance based on the dental applianceidentification information.

An inspection recipe for the plastic shell may be determined based on atleast one of first information associated with the first image of theplastic shell or second information associated with the digital file. Aninspection recipe may specify one or more additional images of theplastic shell, if any, to be generated and may specify settings (e.g.,zoom, orientation, focus, etc.) for the one or more imaging devices. Thefirst information and/or the second information may indicate one or moreof: a shape of the aligner, a size of the aligner, one or more featuresof the aligners, areas of higher risk for defects, one or more defects(e.g., deformation of the aligners, crack, etc.), and the like, whichmay be used to determine the additional images to capture for theinspection recipe. Various defects of the plastic shell may bedetermined based on the first image and/or the one or more additionalimages. For example, when the first image is a top view image of theplastic aligner, deformation of the plastic shell may be detected, asdiscussed further below. If an additional image is a side view of theplastic shell, bubbles that are present on a surface of the plasticshell may be detected and/or an inaccurate cutline may be detected.

The inspection recipe may be dynamically determined based on the firstinformation and/or the second information, or the inspection recipe maybe predetermined for the plastic shell and retrieved from a memorylocation. For example, determining the inspection recipe may includedetermining one or more features (e.g., precision cutline, presence ofattachment wells, angle of a cutline, crowding between teeth, crowdingbetween attachment wells, etc.) of the plastic shell using at least oneof the first information or the second information, and determining theone or more additional images to be generated based on the one or morefeatures. Further, the size and/or shape of the plastic shell may bedetermined from the first information and/or the second information, andsettings (e.g., orientation, zoom, focus) for the imaging devices togenerate the additional images may be determined based on at least theone or more features, the size, and/or the shape of the plastic shell.

In some implementations, to determine the inspection recipe, the digitalfile of the plastic shell may be applied to a model (e.g., predictivemodel, machine learning model, etc.) or to a rules engine as an input.The model or the rules engine may generate an output identifying one ormore locations of the plastic shell that are identified as high riskareas for one or more defects. In one example, the model may be apredictive model that performs a numerical simulation on the digitalfile of the plastic aligner by applying one or more forces on theplastic aligner to simulate a removal process of the plastic alignerfrom a dental arch of a patient. The predictive model may calculate astrain value and a force value that is applied to cause the strainvalue. If either the strain value or the force value exceed a thresholdvalue at a location on the plastic aligner, then that location may bedetermined to be a high risk area for a defect. In another example, themodel may be a machine learning model that is trained to identify thelocations of high risk areas. The machine learning model may be appliedto a first image of the aligner and/or a digital file of the aligner andmay generate an output indicating one or more high risk areas fordefects at locations on the aligner. The rules engine may use one ormore rules that specify one or more locations are high risk areas forone or more defects when one or more features are included at the one ormore locations. The one or more additional images may be determined forthe inspection recipe based on the one or more locations identified asthe high risk areas by the output. Additional details for methods andsystems for identifying particular aligner areas for inspection may befound in U.S. provisional application 62/737,458 filed Sep. 27, 2018,which is incorporated herein by reference in its entirety.

Further determining the inspection recipe may include determiningsettings for the one or more imaging devices to capture the one or moreadditional images based at least on one of the first informationassociated with the first image or the second information associatedwith the digital file. In one example, the first information and/or thesecond information may include a size and/or shape of the plastic shell,which may be used to determine a zoom setting and/or a focus setting forthe one or more imaging devices. In another example, the firstinformation and/or the second information may include a feature (e.g.,an angle of a cutline) of the plastic shell, which may be used todetermine an orientation (e.g., an angle at which to generate an imageof the feature) of one or more imaging devices. Further, if a side viewimage is determined to be generated, the settings may include a numberof images determined to be generated to be composited together to form acomposite two-dimensional (2D) image that is a panoramic view of theside of the plastic aligner, or an image of the side of the plasticshell that is three-dimensional (3D). The settings of the one or moreimaging devices may differ to allow the imaging devices to capturedifferent images, which may enable detecting different defects. Forexample, to detect bubbles in the plastic shell, the zoom setting may beconfigured to a certain micron setting (e.g., 10 microns to 30 microns),whereas another zoom setting may enable detecting another type of defect(e.g., a burr).

The inspection recipe may be performed to capture the one or moreadditional images of the plastic shell using the determined settings. Adetermination may be made whether the one or more defects are includedin the plastic shell based at least on one of the first image and/or theone or more additional images. Quality control may be performed for theplastic shell in response to determining that the one or more defectsare included in the plastic shell.

In some implementations, detecting the defects may include comparingaspects of the digital file of the mold associated with the aligner withaspects of the first image and/or additional images of the aligner. Forexample, an approximated first property (e.g., an approximated outersurface of the aligner) for an aligner may be determined from thedigital file of the mold associated with the aligner. The approximatedfirst property may be determined based on a manipulation of a digitalmodel of a mold used to create the aligner. Also, a second property(e.g., a shape of the aligner in the captured image(s)) of the alignermay be determined from the captured image(s). The approximated firstproperty and the second property may be compared. The comparison mayinclude computing a projection of the approximated outer surface (e.g.,digital model) of the aligner into the same plane as the shape of thealigner and a region between contours of the approximated outer surfaceand the shape of the aligner in the image is identified. If a dimension(e.g., thickness or area) of the region exceeds a threshold, it may bedetermined that the aligner is deformed. If the dimension is within athreshold, then another comparison may be performed that deforms acurvature of a dental arch in the digital model or approximatedprojection towards a curvature of the dental arch of the manufacturedaligner in the aligner image. Once deformed, other contours of thedigital model or other approximated properties may be compared to thealigner image or other images of the manufactured aligner to determinewhether the cutline or other property of the imaged aligner matcheswithin a threshold. If not, the cutline or other property may bedetermined to be flawed. Other aligner properties that may be analyzedmay include debris, webbing, trimmed attachments, and missingattachments, etc. The software may also determine how the manufacturedaligner may rest on a two-dimensional plane and adjust the projection ofthe digital model or approximated property and the image of themanufactured aligner accordingly for the comparison analysis.

In some implementations, the digital models of the aligners may begenerated as part of the manufacturing process of the aligners and thedigital models may be received as inputs. Further, a user interface maybe provided that displays the image based quality control process (e.g.,digital models of aligners, images of the aligners, comparison of thedigital models with the images) and the results (e.g., measurements,classification).

The hardware components may include a platform with a fixed or rotatingtable for aligner positioning and image capture, camera setups (e.g.,positioning), and/or lighting system for uniform exposure and imagecapture with uniform ambient parameters. Further, the hardwarecomponents may also enable automatic feeding of parts (e.g., aligners)into the station in which image based quality control is being performedand sorting at the exit of the station. The images of the aligner may beobtained from one or several projections. As such, the hardwarecomponents may include a fixed or rotating table and one or more cameraswith adjustable positioning. Adjusting the configuration of the hardwarecomponents may enable obtaining aligner images from different angles(e.g., top view, side view, diagonal view, etc.). In one implementation,a first camera may be positioned at an angle that enables capturing atop view image of an aligner being analyzed and a second camera may beconfigured to capture one or more side view or diagonal view imagesbased on settings. The hardware components may also include a blue laserscanner that includes a camera, a background screen, and lighting toobtain an image of the shell. Certain information (e.g., secondproperty) may be extracted from the image. The blue laser may beexercised at a certain angle at the surface of the aligner and maygenerate a blue light beam (e.g., with a wavelength of about 440-490 nm)that is received by the camera to generate an image of the plasticshell. Depth information from the image may be extracted to obtaindesired information (e.g., a second property). The hardware may alsoinclude robot-guided cameras that use a design file to guide the camerasto generate an image about the aligner being analyzed. The image may beof the edge/cutline of the aligner. The hardware may also include anultrasonic device that emits soundwaves to measure aligner thickness.Measuring aligner thickness may enable forming of a quality trendinganalysis and detection of thickness related defects. The hardware mayalso include a stereo image sensor to obtain a three-dimensional (3D)image of the plastic shell. The hardware may also include a confocalmicroscope for obtaining an image of the aligner at various focaldepths. The hardware may also include an X-ray device to scan the clearplastic aligner at various cross-sections and obtain an image of theclear plastic aligner. Certain current and voltage settings may be usedto scan cross-sections of the clear plastic aligner and obtain the imageof the clear plastic aligner.

Some embodiments are discussed herein with reference to orthodonticaligners (also referred to simply as aligners). However, embodimentsalso extend to other types of shells formed over molds, such asorthodontic retainers, orthodontic splints, sleep appliances for mouthinsertion (e.g., for minimizing snoring, sleep apnea, etc.) and/orshells for non-dental applications. Other applications may be found wheninspecting 3D printed palatal expanders, removable mandibularrepositioning devices, and removable surgical fixation devices.Accordingly, it should be understood that embodiments herein that referto aligners also apply to other types of dental appliances. For example,the principles, features and methods discussed may be applied to anyapplication or process in which it is useful to perform image basedquality control for any suitable type of customized devices, such as eyeglass frames, contact or glass lenses, hearing aids or plugs, artificialknee caps, prosthetic limbs and devices, orthopedic inserts, as well asprotective equipment such as knee guards, athletic cups, or elbow, chin,and shin guards and other like athletic/protective devices.

In some embodiments, a mold of a patient's dental arch may be fabricatedand a shell may be formed over the mold. The fabrication of the mold maybe performed by processing logic of a computing device, such as thecomputing device in FIG. 21. The processing logic may include hardware(e.g., circuitry, dedicated logic, programmable logic, microcode, etc.),software (e.g., instructions executed by a processing device), firmware,or a combination thereof. For example, one or more operations may beperformed by a processing device executing a computer aided drafting(CAD) program or module.

To manufacture the molds, a shape of a dental arch for a patient at atreatment stage is determined based on a treatment plan. In the exampleof orthodontics, the treatment plan may be generated based on anintraoral scan of a dental arch to be modeled. The intraoral scan of thepatient's dental arch may be performed to generate a three dimensional(3D) virtual model of the patient's dental arch (mold). For example, afull scan of the mandibular and/or maxillary arches of a patient may beperformed to generate 3D virtual models thereof. The intraoral scan maybe performed by creating multiple overlapping intraoral images fromdifferent scanning stations and then stitching together the intraoralimages to provide a composite 3D virtual model. In other applications,virtual 3D models may also be generated based on scans of an object tobe modeled or based on use of computer aided drafting techniques (e.g.,to design the virtual 3D mold). Alternatively, an initial negative moldmay be generated from an actual object to be modeled (e.g., a dentalimpression or the like). The negative mold may then be scanned todetermine a shape of a positive mold that will be produced.

Once the virtual 3D model of the patient's dental arch is generated, adental practitioner may determine a desired treatment outcome, whichincludes final positions and orientations for the patient's teeth.Processing logic may then determine a number of treatment stages tocause the teeth to progress from starting positions and orientations tothe target final positions and orientations. The shape of the finalvirtual 3D model and each intermediate virtual 3D model may bedetermined by computing the progression of tooth movement throughoutorthodontic treatment from initial tooth placement and orientation tofinal corrected tooth placement and orientation. For each treatmentstage, a separate virtual 3D model of the patient's dental arch at thattreatment stage may be generated. The shape of each virtual 3D modelwill be different. The original virtual 3D model, the final virtual 3Dmodel and each intermediate virtual 3D model is unique and customized tothe patient.

Accordingly, multiple different virtual 3D models may be generated for asingle patient. A first virtual 3D model may be a unique model of apatient's dental arch and/or teeth as they presently exist, and a finalvirtual 3D model may be a model of the patient's dental arch and/orteeth after correction of one or more teeth and/or a jaw. Multipleintermediate virtual 3D models may be modeled, each of which may beincrementally different from previous virtual 3D models.

Each virtual 3D model of a patient's dental arch may be used to generatea unique customized physical mold of the dental arch at a particularstage of treatment. The shape of the mold may be at least in part basedon the shape of the virtual 3D model for that treatment stage. Thevirtual 3D model may be represented in a file such as a computer aideddrafting (CAD) file or a 3D printable file such as a stereolithography(STL) file. The virtual 3D model for the mold may be sent to a thirdparty (e.g., clinician office, laboratory, manufacturing facility orother entity). The virtual 3D model may include instructions that willcontrol a fabrication system or device in order to produce the mold withspecified geometries.

A clinician office, laboratory, manufacturing facility or other entitymay receive the virtual 3D model of the mold, the digital model havingbeen created as set forth above. The entity may input the digital modelinto a rapid prototyping machine. The rapid prototyping machine thenmanufactures the mold using the digital model. One example of a rapidprototyping manufacturing machine is a 3D printer. 3D printing includesany layer-based additive manufacturing processes. 3D printing may beachieved using an additive process, where successive layers of materialare formed in proscribed shapes. 3D printing may be performed usingextrusion deposition, granular materials binding, lamination,photopolymerization, continuous liquid interface production (CLIP), orother techniques. 3D printing may also be achieved using a subtractiveprocess, such as milling.

In some instances, stereolithography (SLA), also known as opticalfabrication solid imaging, is used to fabricate an SLA mold. In SLA, themold is fabricated by successively printing thin layers of aphoto-curable material (e.g., a polymeric resin) on top of one another.A platform rests in a bath of a liquid photopolymer or resin just belowa surface of the bath. A light source (e.g., an ultraviolet laser)traces a pattern over the platform, curing the photopolymer where thelight source is directed, to form a first layer of the mold. Theplatform is lowered incrementally, and the light source traces a newpattern over the platform to form another layer of the mold at eachincrement. This process repeats until the mold is completely fabricated.Once all of the layers of the mold are formed, the mold may be cleanedand cured.

Materials such as a polyester, a co-polyester, a polycarbonate, apolycarbonate, a thermoplastic polyurethane, a polypropylene, apolyethylene, a polypropylene and polyethylene copolymer, an acrylic, acyclic block copolymer, a polyetheretherketone, a polyamide, apolyethylene terephthalate, a polybutylene terephthalate, apolyetherimide, a polyethersulfone, a polytrimethylene terephthalate, astyrenic block copolymer (SBC), a silicone rubber, an elastomeric alloy,a thermoplastic elastomer (TPE), a thermoplastic vulcanizate (TPV)elastomer, a polyurethane elastomer, a block copolymer elastomer, apolyolefin blend elastomer, a thermoplastic co-polyester elastomer, athermoplastic polyamide elastomer, or combinations thereof, may be usedto directly form the mold. The materials used for fabrication of themold can be provided in an uncured form (e.g., as a liquid, resin,powder, etc.) and can be cured (e.g., by photopolymerization, lightcuring, gas curing, laser curing, crosslinking, etc.). The properties ofthe material before curing may differ from the properties of thematerial after curing.

Aligners may be formed from each mold and when applied to the teeth ofthe patient, may provide forces to move the patient's teeth as dictatedby the treatment plan. The shape of each aligner is unique andcustomized for a particular patient and a particular treatment stage. Inan example, the aligners can be pressure formed or thermoformed over themolds. Each mold may be used to fabricate an aligner that will applyforces to the patient's teeth at a particular stage of the orthodontictreatment. The aligners each have teeth-receiving cavities that receiveand resiliently reposition the teeth in accordance with a particulartreatment stage.

In one embodiment, a sheet of material is pressure formed orthermoformed over the mold. The sheet may be, for example, a sheet ofplastic (e.g., an elastic thermoplastic, a sheet of polymeric material,etc.). To thermoform the shell over the mold, the sheet of material maybe heated to a temperature at which the sheet becomes pliable. Pressuremay concurrently be applied to the sheet to form the now pliable sheetaround the mold. Once the sheet cools, it will have a shape thatconforms to the mold. In one embodiment, a release agent (e.g., anon-stick material) is applied to the mold before forming the shell.This may facilitate later removal of the mold from the shell.

Additional information may be added to the aligner. The additionalinformation may be any information that pertains to the aligner.Examples of such additional information includes a part numberidentifier, patient name, a patient identifier, a case number, asequence identifier (e.g., indicating which aligner a particular lineris in a treatment sequence), a date of manufacture, a clinician name, alogo and so forth. For example, after an aligner is thermoformed, thealigner may be laser marked with a part number identifier (e.g., serialnumber, barcode, or the like). In some embodiments, the system may beconfigured to read (e.g., optically, magnetically, or the like) anidentifier (barcode, serial number, electronic tag or the like) of themold to determine the part number identifier associated with the alignerformed thereon. After determining the part number identifier, the systemmay then tag the aligner with the unique part number identifier. Thepart number identifier may be computer readable and may associate thataligner to a specific patient, to a specific stage in the treatmentsequence, whether it's an upper or lower shell, a digital modelrepresenting the mold the aligner was manufactured from and/or a digitalfile including a virtually generated digital model or approximatedproperties thereof of that aligner (e.g., produced by approximating theouter surface of the aligner based on manipulating the digital model ofthe mold, inflating or scaling projections of the mold in differentplanes, etc.). In some embodiments, the virtually generated digitalmodel of the aligner or approximated properties thereof may be comparedto a property (e.g., shape of the aligner) of the manufactured alignerdetermined from an image of the manufactured aligner for image basedquality control, as described in further detail below with reference toFIGS. 1 and 8A and 8B.

After an aligner is formed over a mold for a treatment stage, thataligner is subsequently trimmed along a cutline (also referred to as atrim line) and the aligner may be removed from the mold. The processinglogic may determine a cutline for the aligner. The determination of thecutline(s) may be made based on the virtual 3D model of the dental archat a particular treatment stage, based on a virtual 3D model of thealigner to be formed over the dental arch, or a combination of a virtual3D model of the dental arch and a virtual 3D model of the aligner. Thelocation and shape of the cutline can be important to the functionalityof the aligner (e.g., an ability of the aligner to apply desired forcesto a patient's teeth) as well as the fit and comfort of the aligner. Forshells such as orthodontic aligners, orthodontic retainers andorthodontic splints, the trimming of the shell may play a role in theefficacy of the shell for its intended purpose (e.g., aligning,retaining or positioning one or more teeth of a patient) as well as thefit of the shell on a patient's dental arch. For example, if too much ofthe shell is trimmed, then the shell may lose rigidity and an ability ofthe shell to exert force on a patient's teeth may be compromised.

On the other hand, if too little of the shell is trimmed, then portionsof the shell may impinge on a patient's gums and cause discomfort,swelling, and/or other dental issues. Additionally, if too little of theshell is trimmed at a location, then the shell may be too rigid at thatlocation. In some embodiments, the cutline may be a straight line acrossthe aligner at the gingival line, below the gingival line, or above thegingival line. In some embodiments, the cutline may be a gingivalcutline that represents an interface between an aligner and a patient'sgingiva. In such embodiments, the cutline controls a distance between anedge of the aligner and a gum line or gingival surface of a patient.

Each patient has a unique dental arch with unique gingiva. Accordingly,the shape and position of the cutline may be unique and customized foreach patient and for each stage of treatment. For instance, the cutlineis customized to follow along the gum line (also referred to as thegingival line). In some embodiments, the cutline may be away from thegum line in some regions and on the gum line in other regions. Forexample, it may be desirable in some instances for the cutline to beaway from the gum line (e.g., not touching the gum) where the shell willtouch a tooth and on the gum line (e.g., touching the gum) in theinterproximal regions between teeth. Accordingly, it is important thatthe shell be trimmed along a predetermined cutline.

In some embodiments, a shell may have multiple cutlines. A first orprimary cutline may control a distance between an edge of the shell anda gum line of a patient. Additional cutlines may be for cutting slots,holes, or other shapes in the shell. For example, an additional cutlinemay be for removal of an occlusal surface of the shell, an additionalsurface of the shell, or a portion of the shell that, when removed,causes a hook to be formed that is usable with an elastic.

In some embodiments, a gingival cutline is determined by first defininginitial gingival curves along a line around a tooth (LAT) of a patient'sdental arch from a virtual 3D model (also referred to as a digitalmodel) of the patient's dental arch for a treatment stage. The gingivalcurves may include interproximal areas between adjacent teeth of apatient as well as areas of interface between the teeth and the gums.The initially defined gingival curves may be replaced with a modifieddynamic curve that represents the cutline.

Defining the initial gingival curves along a line around a tooth (LAT)can be suitably conducted by various conventional processes. Forexample, such generation of gingival curves can include any conventionalcomputational orthodontics methodology or process for identification ofgingival curves. In one example, the initial gingival curves can begenerated by use of the Hermite-Spline process. In general, the Hermiteform of a cubic polynomial curve segment is determined by constraints onendpoints P₁ and P₄ and tangent vectors at endpoints R₁ and R₄. TheHermit curve can be written in the following form:Q(s)=(2s ³−3s ²+1)P ₁+(−2s ³+3s ²)P ₄+(s ³−2s ² +s)R ₁+(s ³ −s ₂)R ₄;s[0,1]  (1)Equation (1) can be rewritten as:Q(s)=F ₁(s)P ₁ +F ₂(s)P ₄ +F ₃(s)R ₁ +F ₄(s)R ₄;  (2)Wherein equation (2) is the geometric form of the Hermite-Spline Curve,the vectors P₁, P₄, R₁, R₄ are the geometric coefficients, and the Fterms are Hermite basis functions.

A gingival surface is defined by gingival curves on all teeth and a baseline, with the base line being obtained from a digital model of thepatient's dental arch. Thus, with a plurality of gingival curves andbase line, a Hermite surface patch that represents the gingival surfacecan be generated.

Rather than having a cutline that causes a sharp point or other narrowregion in the interproximal areas between teeth that can cause weakeningof the aligner material during use, the initial gingival curves may bereplaced with a cutline that has been modified from the initial gingivalcurves. The cutline can be generated to replace the initial gingivalcurves by initially obtaining a plurality of sample points from a pairof gingival curve portions residing on each side of an interproximalarea. The sample points are then converted into point lists withassociated geometric information (e.g., into the Amsterdam DentistryFunctional (ADF) format or other like data formats). Sample points maybe suitably selected proximate the inner region between two teeth, butsufficiently distanced from where the two teeth meet or come to a point(or the separation between the two teeth narrows) within aninterproximal area between the two teeth.

The collection of sample points provides a plurality of points in space(not in the same plane) that can be used to generate an average planeand a vector that is normal to the average plane. Sample points that areassociated with gingival curve portions can then be projected onto theaverage plane to generate two new curves. To minimize weakening of aregion of the aligner material within the interproximal area, themodified dynamic curve can be configured with an offset adjustment thatcomprises a minimum radius setting in the interproximal area to preventbreakage of the aligner material during use. The offset adjustment isfurther configured to ensure that a resulting cutline have a sufficientradius in the interproximal area to facilitate enough resistance forceapplied to the teeth to cause effective movement, but not too smallradius as to facilitate breakage. For example, a sharp point or othernarrow portion of material can create a stress region susceptible tobreak during use, and so should be avoided. Accordingly, rather thanhave the cutline comprise a sharp point or other narrow region, aplurality of intersection points and tangent points may be used togenerate a cutline in the interproximal region between adjacent teeththat maintains structural strength of the aligner and prevents sharppoints and/or narrow portions that could break. In one embodiment, thecutline is spaced apart from the gingival surface at regions where thealigner will contact a tooth and is designed to at least partially toucha patient's gingival surface in one or more interproximal regionsbetween teeth.

After outline determination, the aligner may then be cut along theoutline (or outlines) using markings and/or elements that were imprintedin the aligner. In some embodiments, the aligner may be manually cut bya technician using scissors, a bur, a cutting wheel, a scalpel, or anyother cutting implement. In another embodiment, the aligner is cut alongthe outline by a computer controlled trimming machine such as a CNCmachine or a laser trimming machine. The computer controlled trimmingmachine may include a camera that is capable of identifying the outlinein the aligner. The computer controlled trimming machine may use imagesfrom a camera to determine a location of the outline from markings inthe aligner, and may control an angle and position of a cutting tool ofthe trimming machine to trim the aligner along the outline using theidentified markings.

Additionally, or alternatively, the aligner may include coordinatesystem reference marks usable to orient a coordinate system of thetrimming machine with a predetermined coordinate system of the aligner.The trimming machine may receive a digital file with trimminginstructions (e.g., that indicate positions and angles of a laser orcutting tool of the trimming machine to cause the trimming machine totrim the aligner along the outline). By aligning the coordinate systemof the trimming machine to the aligner, an accuracy of computercontrolled trimming of the aligner at the outline may be improved. Thecoordinate system reference marks may include marks sufficient toidentify an origin and an x, y and z axis.

Prior to trimming the aligner a technician may apply a dye, a coloredfiller, or other material to the aligner to fill in slight indentationsleft by one or more elements imprinted in the aligner. The dye, coloredfiller, etc. may color the slight indentations without coloring aremainder of the aligner. This may increase a contrast between theoutline and the remainder of the aligner. Additional polishing (e.g., ofedges) and/or removal of undesired artifacts may be performed after thealigner is removed from the mold and trimmed. After trimming, thealigner may be removed from the mold.

In the embodiments disclosed herein, each aligner or other dentalappliance (e.g., removable surgical fixation devices, removablemandibular repositioning appliances, removable palatal expanders) thatis manufactured may be sent to an image based quality control (IBQC)station that detects one or more quality issues (e.g., deformation) withthe aligners. Alternatively, aligners that are flagged for qualityinspection may be sent to the IBQC station. For example, the digitalfiles of aligners may be input into a machine learning model, numericalsimulation, rules engine and/or other module to determine whether thereis an increased chance that any of those aligners will have defects. Themachine learning model, numerical simulation, rules engine and/or othermodule may identify a subset of the aligners that are to be inspectedusing the IBQC system. Optionally, the IBQC systems and methods mayclassify the inspected aligners as deformed, possibly deformed, or notdeformed, and may also provide a recommendation (e.g., requiring furtherinspection, requires remanufacturing, approved, etc.) including theresults of its analysis.

Turning now to the figures, FIG. 1A illustrates a flow diagram for amethod 100 of performing image based quality control for a shell (e.g.,an orthodontic aligner), in accordance with one embodiment. One or moreoperations of method 100 are performed by processing logic of acomputing device. The processing logic may include hardware (e.g.,circuitry, dedicated logic, programmable logic, microcode, etc.),software (e.g., instructions executed by a processing device), firmware,or a combination thereof. For example, one or more operations of method100 may be performed by a processing device executing an image basedquality control module 2150 of FIG. 21. It should be noted that themethod 100 may be performed for each unique aligner that is manufacturedfor each patient's treatment plan or for a subset of unique aligners.

At block 102, processing logic may receive a digital file associatedwith a plastic shell that is customized for a dental arch of a patient.The dental appliance identification information may be captured by acamera and interpreted by the inspection system. Optionally, atechnician may also manually input this information so that theinspection system can retrieve the digital file. In still furtherembodiments, a dental appliance sorting system may sort a series ofdental appliances in a known order. The inspection system may retrievethe dental appliance order from the dental appliance sorting system inorder to know which of the dental appliances are currently beinginspected and the order in which they arrive at the station. Optionally,the dental appliance may arrive at the inspection system in a traycarrying the dental appliance identifying information (e.g., RFID tag,barcode, serial numbers, etc.), which is read by the inspection system.Thereafter, the inspection system may retrieve the digital fileassociated with the dental appliance based on the dental applianceidentification information received by the system.

In some embodiments, the digital file may include a digital model of theplastic shell. In some embodiments, the digital file associated with theplastic shell may include a digital model of a mold used to manufacturethe plastic shell. A digital model of the plastic shell may be obtainedby manipulating the digital model of the mold and approximating a firstproperty of the plastic shell. For example, in some embodiments, asurface of the mold may be enlarged, inflated, or otherwise offset toapproximate a surface (inner and/or outer surface) of the plastic shell.In some instances, the surface(s) of the mold associated with the teethand/or attachments and/or virtual fillers of the patient are enlarged,inflated, or offset. Optionally, an inner or outer surface of theplastic shell is determined and the other surface is approximated basedon a thickness of the material used to form the plastic shell. In somecases where the shell is to be formed by thermoforming a sheet ofmaterial over a physical mold, the approximated surfaces may take intoaccount stretching and thinning of the material over certain parts ofthe mold.

At block 104, processing logic may generate a first image of the plasticshell using one or more imaging devices. The first image may be a topview image, a side view image, or a diagonal image and may include atleast one of a photographic image or an X-ray image, or other digitalimage (e.g., an ultrasound image). The one or more imaging devices mayinclude at least one of a camera, a blue laser scanner, a confocalmicroscope, a stereo image sensor, an X-ray device, and/or an ultrasonicdevice.

In some implementations, two cameras may be used with certain lighting,a backing screen, a mirror, and/or X-Y stages to capture the first imageand/or additional images, as described further below with reference toFIG. 2. For example, the first image may be captured by a first top viewcamera and the first image may be used to determine an inspection recipeas described below. The top view image (shown in FIG. 3A) may be used todetermine a motion control and/or a screen path for a backing screenbetween a front side and a backside of the plastic shell identified inthe first image (shown in FIG. 3B). The motion control and screen pathmay be used to place the backing screen while additional images (e.g.,side view images) specified in the inspection recipe are captured. Insome embodiments, the digital file may be used to guide the one or moreimaging devices (e.g., using robot guided image acquisition) to capturethe first image by tracing an edge and/or cutline of the plastic shellbased on a digital model of the plastic aligner included in the digitalfile. Further, in some implementations, processing logic may analyze thedigital file associated with the plastic shell to determine one or morefeatures included in the plastic shell and configure settings of the oneor more imaging devices to capture the first image at a locationassociated with the one or more features.

At block 106, processing logic may determine an inspection recipe forthe plastic shell based on at least one of first information associatedwith the first image of the plastic shell or second informationassociated with the digital file. The inspection recipe may specify oneor more additional images of the plastic shell to be generated. Forexample, the first information associated with the first image of theplastic shell or the second information associated with the digital filemay indicate a size and/or shape of the plastic shell, and processinglogic may determine to capture one or more additional images using aparticular zoom setting, focus setting, and/or orientation (e.g., angle,position, etc.) setting. If the first information indicates the plasticshell is small in size, then processing logic may determine to capturean additional image at a zoomed-in image device setting. Additionally,in some embodiments the first information associated with the firstimage of the plastic shell or the second information associated with thedigital file may indicate the presence of certain features (e.g., aprecision cutline of the plastic aligner, cavities of the plasticaligner associated with attachments, an angle of the cutline, a distancebetween cavities of the plastic aligner associated with teeth, or adistance between cavities of the plastic aligner associated withattachments). Processing logic may determine the inspection recipe toinclude one or more additional images of the plastic shell based on theidentified features, as further discussed with reference to FIG. 5.

Processing logic may also determine the settings of the imaging devicesto use to capture the one or more additional images. The settings may bebased on the size, shape, and/or features identified in the plasticshell. For example, the settings may include at least one of one or morelocations for the one or more imaging devices to generate the one ormore additional images, one or more orientations for the one or moreimaging devices to capture the one or more additional images, one ormore depths of focus for the one or more imaging devices to capture theone or more additional images, and/or a number of the one or moreadditional images for the one or more imaging devices.

Further, in some embodiments, processing logic may determine theinspection recipe by applying the digital file associated with theplastic shell to a model (e.g., a trained machine learning model or anumerical simulation) as input, and the model may output one or morelocations of the plastic shell that are identified as high risk areasfor defects, as described below further with reference to FIG. 6. Inaddition, in some embodiments, processing logic may determine theinspection recipe by applying the digital file associated with theplastic shell to a rules engine that uses one or more rules that specifycapturing one or more additional images at one or more locations of theplastic shell when one or more features are present at the one or morelocations, as described below further with reference to FIG. 7.

In some embodiments, determining the inspection recipe may includeretrieving the inspection recipe from a memory location of the computersystem depicted in FIG. 21. Settings for the one or more imaging devicesto generate the one or more additional images may be preset in theinspection recipe retrieved from the memory location. The settings mayinclude at least one of one or more locations for the one or moreimaging devices to generate the one or more additional images, one ormore orientations for the one or more imaging devices to capture the oneor more additional images, one or more depths of focus for the one ormore imaging devices to capture the one or more additional images,and/or a number of the one or more additional images for the one or moreimaging devices.

At block 108, processing logic may perform the inspection recipe tocapture the one or more additional images of the plastic shell. In someembodiments, performing the inspection recipe to capture the one or moreadditional images of the plastic shell may include configuring settingsof the one or more imaging devices to capture the one or more imagesbased at least on the first information associated with the first imageor the second information associated with the digital file. The settingsmay include at least one of an orientation of the one or more imagingdevices, a location of the one or more imaging devices, a zoom of theone or more imaging devices, or a depth of focus of the one or moreimaging devices. In some embodiments, performing the inspection recipemay include tracing, using an imaging device, an edge of the plasticshell using data from the design file of the plastic shell or from amotion control and screen path determined from the first image tocapture a subset of images of the one or more additional images thatrepresent a cutline of the plastic shell.

At block 110, processing logic may determine whether there are one ormore defects included in the plastic shell based on the first imageand/or the one or more additional images. If there are not any defectsincluded, the method may conclude. In some embodiments, determiningwhether there are defects may include obtaining the digital fileassociated with the plastic shell, determining an approximated firstproperty (or intended property) for the plastic shell from the digitalfile, determining a second property (or actual property) of themanufactured plastic shell from the first image and/or the one or moreadditional images, and comparing the approximated first/intendedproperty to the second/actual property. If the approximated firstproperty and the second property vary by a threshold amount, then adefect may be determined to be present in the plastic shell. In someapplications, the digital file associated with the plastic shellcomprises a digital model of the mold and/or a digital model of theshell. Optionally, determining intended properties for the plastic shellcomprises manipulating the digital model of the mold, examples of whichare provided throughout. For example, the intended property may bedetermined by manipulating a surface of the digital model of the mold toapproximate an outer surface of the manufactured dental appliance. Insome instances, an intended property for the dental appliance may be aprojection or silhouette of the intended outer surface of the dentalappliance into a plane. In some embodiments, the approximated firstproperty may be a virtual cutline of the digital model of the alignerand the second property may be an actual cutline from the first image ofthe plastic shell. It should be noted that “cutline” and “edge” may beused interchangeably herein. An example of determining the secondproperty from the additional images of the inspection recipe that arecaptured and comparing the second property to the approximated firstproperty to determine whether any defects are present is shown in FIGS.4A-4C.

If it is determined at block 110 that there are one or more defectsincluded in the plastic shell, then at block 112 processing logic mayperform quality control for the plastic shell. For example, processinglogic may classify the plastic shell as defective and specify one ormore remedies (e.g., add filler material, smooth cutline, modificationsto one or more attachments on the mold or attachment cavities of thedental appliance, remanufacture, etc.) to attempt to remove the one ormore defects. Examples of quality control operations are described ingreater detail below.

FIG. 1B illustrates a flow diagram for a method 120 of performing imagebased quality control for a shell (e.g., an orthodontic aligner), inaccordance with one embodiment. One or more operations of method 120 areperformed by processing logic of a computing device. The processinglogic may include hardware (e.g., circuitry, dedicated logic,programmable logic, microcode, etc.), software (e.g., instructionsexecuted by a processing device), firmware, or a combination thereof.For example, one or more operations of method 120 may be performed by aprocessing device executing an image based quality control module 2150of FIG. 21. It should be noted that the method 120 may be performed foreach unique aligner that is manufactured for each patient's treatmentplan or for a subset of unique aligners.

At block 122, processing logic may generate a first image of a plasticshell using one or more imaging devices. In some embodiments, the firstimage may be a top view image of the plastic shell. The one or moreimaging devices may include a camera, an X-ray device, a blue laserscanner, or the like.

At block 124, processing logic may determine whether there are one ormore defects detected in the plastic shell based on the first image. Ifthere are no defects detected, the method 120 may conclude. The one ormore defects may be detected by comparing the first image of the plasticshell to a digital file including a digital model of the plastic shell.For example, processing logic may compare the top view image of thefirst shell to a top view of the digital model of the plastic shell todetermine whether a shape of the plastic shell is deformed. If one ormore differences between a shape of plastic shell from the top viewimage and a shape of the plastic shell from the digital model of theshell exceeds a threshold, then a defect may be determined to beincluded in the plastic shell. The first image may be applied to atrained machine learning model that is trained to identify high riskareas for defects, or to a rules engine that includes rules specifyingthat certain features at locations indicate high risk areas for defects.

If there are one or more defects detected or possible defects detected(high risk areas for defects) in the plastic aligner, at block 126,processing logic may determine an inspection recipe for the plasticshell based on at least the one or more defects. Processing logic maydetermine one or more additional images to generate for the inspectionrecipe and settings for the one or more imaging devices to use tocapture the one or more additional images. For example, if the plasticshell is determined to be deformed based on the top view image,processing logic may determine to capture one or more side view imagesof the plastic shell at the deformation location to further analyze(e.g., verify and/or detect other defects) the plastic shell. Thesettings determined may include zooming in on the detected defects atcertain locations, adjusting focus depth to verify the defect and/ordetect other defects, etc. Once the inspection recipe is determined,processing logic may perform the inspection recipe by capturing the oneor more additional images using the determined settings.

At block 128, processing logic may analyze the one or more additionalimages to verify the defect detected and/or the possible defect in thefirst image and/or to detect one or more new defects and/or possibledefects in the plastic shell. In some embodiments, processing logic maycompare the additional images to similar representations (e.g., similarzoom settings, focus settings, etc.) of the plastic aligner in thedigital model to determine any differences and/or verify the defectdetected based on the first image. In some embodiments, processing logicmay detect new defects (e.g., a crack) and/or possible defects based onthe additional images. The additional images may be applied to themachine learning model and/or the rules engine, as described above.

At block 130, processing logic may determine whether the defectsdetected and/or the possible defects detected in the first image areverified based on the one or more additional images and/or whether anynew defects and/or possible defects are detected. If the one or moredefects detected and/or the possible defects detected in the first imageare not verified and/or there are no new defects detected and/orpossible defects detected in the one or more additional images, themethod 120 may conclude. If the defects detected and/or possible defectsdetected in the first image are verified based on the one or moreadditional images and/or there are new defects detected and/or possibledefects detected based on the one or more additional images, at block132, processing logic may perform quality control for the plastic shell.

FIG. 2 illustrates an example imaging system 200 including a top viewcamera 202 and a side view camera 204, in accordance with oneembodiment. The imaging system 200 may be used to extract the cutline ofthe plastic shell 206 being analyzed to determine whether there is adefect present by comparing the cutline of the plastic shell with avirtual cutline obtained from a digital model of the plastic shell 206.The plastic shell 206 may be secured in a stationary position by aplatform part holder 208. The top view camera 202 may be configured toacquire a top view image 300 of the clear plastic shell 206 usingcertain illumination settings to enable the clear plastic aligner 206 tobe visible in the top view image 300, as depicted in FIG. 3A, inaccordance with an embodiment. Processing logic may obtain an outline302 of a projection or silhouette of the plastic shell, as depicted inFIG. 3B, in accordance with an embodiment. The side view camera 204 maybe used to acquire front and back side views of the plastic aligner byrotating around the plastic aligner 206 as it is held by the platformpart holder 208 or by the plastic aligner 206 being rotated as the sideview camera 204 remains stationary. In some embodiments, the plasticaligner 206 may not be secured by a platform part holder 208 and theplastic aligner 206 may be stationary on the platform while the sideview camera 204 takes multiple images around the sides of the plasticaligner 206. The cameras 202 and 204 may be static and placed away froma conveyor path in some embodiments. The imaged plastic shell 206 may beplaced on an x-y-z-θ (4 axes of motion control) platform or stage insome embodiments.

The imaging system 200 may acquire separate front and back side viewimages without stray light interference from the side not currentlyunder inspection by using a backing screen 210 in embodiments. Thebacking screen 210 may be inserted into the gap between the front(buccal) and back (lingual) side of the plastic aligner. A motioncontrol and screen path 304 is determined for the plastic shell 206 byidentifying points between the front side and the back side of theplastic aligner that enable a screen path to be generated such that thebacking screen 210 does not touch the plastic aligner throughout thescreen path. Processing logic may detect a center 306 of the plasticaligner and adjust the motion control and screen path parametersaccordingly. Further, the motion control speed may be high enough toachieve an inspection cycle within a target time period (e.g., 10-20seconds) for both the front side and the back side of the plasticaligner. A mirror 212 may be used as a deflector to capture the imagesfrom the back side or the front side of the plastic aligner as it isheld in the platform part holder 208 in embodiments. The mirror 212 maybe angled at a certain degree (e.g., 45°, 50°, 55°, etc.) and may beused in combination with a light source to enable images to be capturedthat profile the cutline of the front (buccal) side and the cutline ofthe back (lingual) side of the plastic aligner.

In some embodiments, the imaging system 200 may not use a backing plateto prevent light from the front row teeth from interfering with imagingthe back row teeth. In some embodiments, the imaging system 200 can usea focused light to acquire the cutline of the plastic shell 206. Forexample, the focused light may be used to illuminate just the cutline(e.g., buccal or lingual) that is currently being inspected withoutstray light interference from other cutlines that are simultaneously inthe camera's field of view (e.g., prevent the buccal cutline and thelingual cutline interfering with each other). In such an embodiment, thetop view camera 202 may capture a top view image of the plastic shell206 and extract a top view contour. In some embodiments, the plasticshell 206 may be placed within the field of view of the top view camera202 and the imaging system 200 may align the plastic shell 206 tocapture the top view image.

The top view image may be used to determine an inspection recipeincluding one or more side view images. Using the top view contour,contour x-y points may be transmitted to the side view camera 204. Insome embodiments, properties of the camera, such as zoom and/or focusdepth may be determined for the inspection recipe depending on whichside of the plastic shell 206 is being captured. For example, the focusarea of the side view camera 204 may be adjusted when the buccal side isfarther away to accurately capture the lingual side of the plastic shell206 without interference. Further, the top view image may be used torotate the plastic shell 206 to proper orientation so the cutline underinspection is facing the side view camera 204. In some embodiments, therotary motion required to rotate the plastic shell 206 may occursimultaneously with the x-y motion of the side view camera 204 and maynot affect inspection time.

Once the inspection recipe is determined, imaging of the cutline maybegin. A small cylindrical beam of light may be moved along the contour(local structured illumination or SLI) using the contour x-y points sothat just the cutline above the light path is illuminated. An x-y-rotarystage motion control system or a multi-axis robot arm may be used toacquire the images. In some embodiments, the plastic shell 206 may reston a glass platform and the cylinder of light may illuminate from belowthe glass platform to avoid total internal reflection from channelinglight to the other row in the field of view.

FIG. 22A illustrates an example side view image 2200 captured without abacking screen and without structured light illumination. As depicted,the side view camera captured cutline 2202 of a first side (e.g.,buccal) of an aligner without structure light, which causes a cutline2204 of the opposing side (e.g., lingual) of the aligner to be light upand interfere with proper cutline detection.

FIG. 22B illustrates an example side view image 2210 captured without abacking screen using structured light illumination (e.g., a focusedlight), in accordance with one embodiment. As depicted, the side viewcamera captured cutline 2212 of a first side (e.g., buccal) of analigner using directional structure light illumination (LSI), which maysuppress the cutline of the opposing side (e.g., lingual) and may allowreliable cutline detection. LSI may enable reliable cutline detection ininstances where a channel between sides of an aligner are too narrowand/or titled to allow insertion of a backing screen.

FIG. 23 illustrates an example of crack detection in the contour of animage 2300 of an aligner captured using a focused light, in accordancewith one embodiment. As depicted, the focused light used directionalstructured light illumination to capture the side view image 2300, whichproduces a high contrast cutline definition of the cutline 2302. Thehigh contrast may be produced by the light hitting the cutline surfacein a perpendicular or near-perpendicular angle. The cutline 2302captured can be analyzed and a crack 2304 can be identified using thedisclosed techniques. In some embodiments, depth of focus (e.g., 20microns-80 microns) may be configured to provide desired images.

In embodiments using the backing screen 210 or the focused light withouta backing screen, numerous images of cutline may be captured and“stitched” or registered together to form a composite image 400. Thecomposite image 400 may be a panoramic view of each of the front andback of the plastic shell in some embodiments. The side view images forthe front and back may be stitched together to illustrate the cutline ina single plane, which may resemble unfolding or unwrapping of thecutline of the plastic aligner. For example, FIG. 4A depicts a side viewcomposite image 400 of a back side of a shell in accordance with anembodiment. The side view composite image 400 includes three images 402,404, and 406 that are stitched together in a linear fashion. Each of theimages 402, 404, and 406 are captured at a 20 micron pixel resolution inthe illustrated example. Any suitable number of images (e.g., 20) may betaken to produce the front side composite image and the back sidecomposite image.

FIG. 4B illustrates an example edge 408 (e.g., second property) detectedusing the side view composite image 400 of the back side of the shell inFIG. 4A, in accordance with one embodiment. The edge 408 may be detectedby tracing the cutline in the side view composite image 400 to obtain aline representing the edge 408. FIG. 4C illustrates an examplecomparison of an edge 410 detected from a side view composite image witha virtual or intended edge (e.g., approximated first property) 412 ofthe plastic shell from a digital model of the plastic shell, inaccordance with one embodiment. In some embodiments, the intended edge412 is determined by unwrapping a virtual cutline for the dentalappliance into the plane in order to make the comparison to edge 410.The edge 410 and the virtual edge 412 may be overlaid and differencesbetween the two edges may be determined. If the differences exceed athreshold, then a defect may be determined to be included in the plasticaligner and quality control may be performed.

FIG. 5 illustrates a flow diagram for a method 500 of determining aninspection recipe based on features of a plastic shell, in accordancewith one embodiment. One or more operations of method 500 are performedby processing logic of a computing device. The processing logic mayinclude hardware (e.g., circuitry, dedicated logic, programmable logic,microcode, etc.), software (e.g., instructions executed by a processingdevice), firmware, or a combination thereof. For example, one or moreoperations of method 500 may be performed by a processing deviceexecuting an image based quality control module 2150 of FIG. 21. Itshould be noted that the method 500 may be performed for each uniquealigner that is manufactured for each patient's treatment plan.

At block 502, processing logic may determine the inspection recipe basedon at least one of first information associated with a first image ofthe plastic shell or second information associated with a digital fileassociated with the plastic shell. In some embodiments, the first imagemay be a top view image, a side view image, or a diagonal image of theplastic shell. The first information associated with the first imageand/or the second information associated with the digital file mayinclude a size of the plastic shell, a shape of the plastic shell,and/or one or more features of the plastic shell. The one or morefeatures may include at least one of a precision cutline of the plasticaligner, a cavity of the plastic aligner associated with an attachment,an angle of a cutline of the plastic aligner, a distance betweencavities of the plastic aligner associated with teeth (e.g., teethcrowding), a distance between cavities of the plastic aligner associatedwith attachments (e.g., attachment crowding), a distance between a frontside and a back side of the plastic shell, or a thickness of the plasticshell. Block 502 may include performing operations of blocks 504, 506,508, and 510.

At block 504, processing logic may determine one or more features of theplastic shell using at least one of the first information or the secondinformation. At block 506, processing logic may determine the one ormore additional images to be generated for the inspection recipe basedon the one or more features. As described further below, thedetermination to generate the additional images for the inspectionrecipe may be predetermined for a plastic shell or dynamically madeusing a model and/or a rules engine. In an example, if the plasticaligner includes a precision cutline as a feature, processing logic maydetermine to generate an additional image at a location associated withthe precision cutline. If an angle of the cutline is above a certainthreshold angle, processing logic may determine to generate anadditional image at a location associated with the cutline having theexcessive angle. If a distance between teeth is more than a threshold,processing logic may determine to generate an additional image at alocation of the plastic shell associated with those teeth because theplastic may be thinner and more susceptible to cracking at thatlocation.

At block 508, processing logic may determine a size of the plastic shellfrom at least one of the first information or the second information. Insome embodiments, processing logic may also determine a shape of theplastic shell from at least one of the first information or the secondinformation. At block 510, processing logic may determine settings togenerate the one or more additional images based on at least one of theone or more features, the size of the plastic shell, or the shape of theplastic shell. The settings may include at least one of an orientationof the one or more imaging devices, a zoom of the one or more imagingdevices, or a focus of the one or more imaging devices. For example,plastic shells that are small in size may cause the zoom setting to begreater and a depth of focus to be greater. If there is a cavityassociated with an attachment identified by the first information or thesecond information, then the orientation (e.g., positioning and angle)of the camera may be configured to capture a suitable image of thecavity.

In some embodiments, the settings may be predetermined for a first setof additional images that are generated by default for each alignerand/or the settings may be dynamically configured for a second set ofadditional images that are determined dynamically for the inspectionrecipe. For example, some defects, such as bubbles on the surface of theplastic shell may be detected at a certain depth of focus (e.g., 20microns), and thus, a predetermined set of images may be configured tobe captured on the front side and back side of the plastic shell todetermine whether bubbles are present.

At block 512, processing logic may perform the inspection recipe bycapturing the one or more additional images using the settings. The oneor more additional images may be analyzed to determine whether one ormore defects are included in the plastic shell. If so, then qualitycontrol may be performed for the plastic shell.

FIG. 6 illustrates a flow diagram for a method 600 of determining one ormore additional images to generate based on output from a model, inaccordance with one embodiment. One or more operations of method 600 areperformed by processing logic of a computing device. The processinglogic may include hardware (e.g., circuitry, dedicated logic,programmable logic, microcode, etc.), software (e.g., instructionsexecuted by a processing device), firmware, or a combination thereof.For example, one or more operations of method 600 may be performed by aprocessing device executing an image based quality control module 2150of FIG. 21. It should be noted that the method 600 may be performed foreach unique aligner that is manufactured for each patient's treatmentplan.

At block 602, processing logic may apply the digital file to a model asan input. The model may allow targeted inspection by identifying highrisk areas for defects in the plastic shell. For example, at block 604,processing logic may generate, by the model, an output identifying oneor more locations of the plastic shell that are identified as high riskareas of the one or more defects. At block 606, processing logic maydetermine the one or more additional images to generate for theinspection recipe based on the one or more locations identified as thehigh risk areas by the output.

In some implementations, the model may be a machine learning model thatis trained to identify one or more high risk areas for one or moredefects at one or more locations of the plastic shell. Processing logicmay train a machine learning model to generate the trained machinelearning model. A machine learning model may refer to a model artifactthat is created by a training engine using training data (e.g., traininginput and corresponding target outputs). Training may be performed usinga set of training data including at least one of a) digital files of afirst set of plastic aligners with labels indicating whether or not eachof the first set of plastic aligners experienced one or more defects orb) digital files of a second set of plastic aligners with labelsindicating whether or not each of the second set of plastic alignersinclude one or more probable defects. Actual defects for aligners may bereported by manufacturing technicians, by an automated manufacturingsystem and/or by patients. Such historical data on actual defects onphysical aligners may then be added as labels or metadata to theassociated digital files of the aligners and/or images of the aligners.Probable defects for a digital file of an aligner may be determined byprocessing the digital design of the model using a numerical simulation,as described further below. For example, digital files of aligners maybe processed using a numerical simulation to determine probable defects.Digital files of aligners with associated defects (as provided by realworld data) and digital files of aligners with associated probabledefects (as provided by an output of a numerical simulation) may be usedtogether to generate a robust machine learning model that can predictprobable defects of new aligners from digital files of those aligners.

The machine learning model may be composed of a single level of linearor non-linear operations (e.g., a support vector machine (SVM) or asingle level neural network) or may be a deep neural network that iscomposed of multiple levels of non-linear operations. Examples of deepnetworks and neural networks include convolutional neural networksand/or recurrent neural networks with one or more hidden layers. Someneural networks may be composed of interconnected nodes, where each nodereceives input from a previous node, performs one or more operations,and sends the resultant output to one or more other connected nodes forfurther processing.

As mentioned, the information pertaining to whether the plastic alignersexperienced defects may be obtained from historical patient feedback.For example, patients may provide a report that specifies the plasticaligner defect and the location of the defect may be determined (e.g.,from the report, from scanning the aligner, etc.). Also, the patient mayspecify which aligner (e.g., top or bottom) at a particular stage of thetreatment plan failed. In some instances, the patient may return thedefective aligner to a site and the defective aligner may be scanned atthe site to obtain an image of the digital model of the plastic alignerincluding the location of defect. As such, images of the defectivealigners may be collected for image corpora (a set of image corpus,which may include a large set of images) and used as part of thetraining data. Information provided by the patient about the defectivealigner or determined via scanned images may be correlated to determinethe ID of the aligner, which can then be used to obtain the digital fileof that particular aligner. The location of the defect may be placed inthe digital file of the plastic aligner with a label indicating there isa defect at that location.

The digital file may be applied to a predictive model that usesnumerical simulation as an input. Numerical simulation may be performedon the digital file of the plastic aligner to simulate one or moreforces on the plastic aligner. In some embodiments, the forces simulateremoving the aligner from teeth or the mold. The numerical simulationcan determine when an amount of force required to remove the alignerfrom a mold or dental arch reaches a stress or strain level at any pointon the plastic aligner that exceeds a threshold value, which mayindicate that the particular point will crack. In some embodiments, astrain or stress threshold may be used during the numerical simulationto determine when a point on the digital design of the aligner willlikely fail. In this way, the numerical simulation may operate as apredictive model that predicts probable defects on the digital file ofthe aligner by identifying one or more high risk areas for the defects.This simulation may be run dynamically on the digital file of theplastic aligner to identify high risk areas for defects to allowtargeted inspection at those locations. Further, these simulations maybe run numerous times on multiple digital files of plastic aligners andlabels may be included with the digital files indicating whether or notthe digital files include one or more probable points of failure. Thedigital files including the labels indicating whether the digital fileincludes the one or more probable defect may be used as input to trainthe machine learning model.

The numerical simulation may include finite element method, finitedifference method, finite volume method, meshfree methods, smoothparticle galerkin method, combinations of these methods, or the like.Finite element method (also referred to as finite element analysis) mayrefer to a numerical method for solving a structural problem related toan aligner by yielding approximate values of the unknowns at a discretenumber of points over a domain using a series of partial differentialequations. Finite difference method may refer to a numerical method forsolving differential equations by approximating them with differenceequations and calculating approximate values at discrete points. Finitevolume method may refer to a method for representing and evaluatingpartial differential equations in the form of algebraic equations.Finite volume method may also calculate values (e.g., strain, force) atdiscrete places on a meshed geometry of the digital design of thealigner. “Finite volume” may refer to the small volume surrounding eachpoint on a mesh. Meshfree methods may refer to methods that are based oninteraction of nodes or points with all of the neighboring nodes orpoints. In other words, meshfree methods do not require connectionbetween nodes of the simulation domain. The smooth particle galerkinmethod may be a form of a meshfree method.

FIG. 7 illustrates a flow diagram for a method 700 of determining theinspection recipe using a rules engine, in accordance with oneembodiment. One or more operations of method 700 are performed byprocessing logic of a computing device. The processing logic may includehardware (e.g., circuitry, dedicated logic, programmable logic,microcode, etc.), software (e.g., instructions executed by a processingdevice), firmware, or a combination thereof. For example, one or moreoperations of method 700 may be performed by a processing deviceexecuting an image based quality control module 2150 of FIG. 21. Itshould be noted that the method 700 may be performed for each uniquealigner that is manufactured for each patient's treatment plan.

At block 702, processing logic may generate one or more rules for arules engine. The rules may be generated based on at least one of a)historical data (e.g., images of defective aligners, reports, etc.)including reported defects of a set of plastic shells and locations ofthe reported defects on the set of plastic shells, b) digital files of aset of plastic shells with labels indicating whether or not each of theset of plastic shells experienced a defect, or c) digital files of a setof plastic shells with labels indicating whether or not each of the setof plastic shells include a probability that a defect is present in theplastic shell. The rules may be determined based on observations, outputof the numerical simulation, or the like. For example, customers mayprovide reports that describe an aligner that broke during removal orincludes another defect, manufacturing technician may observe alignerbreakage during removal of the aligners from molds, and so forth.Hundreds or thousands of observations of aligners that include defectsmay be used to determine patterns or combinations of features includedin the defective aligners that may have caused the defect. The rules maybe determined that specify there is a probable defect when the patternsor combinations of features are present in subsequent designs. Further,the numerical simulation may be executed and identify probable defectsas output. The output from hundreds or thousands of numericalsimulations may be aggregated and patterns or combinations of featuresmay be identified that are associated with the probable defects. Therules may be determined that specify there is a probable defect when thepatterns or combinations of features are present in subsequent designs.

At block 704, processing logic may determine the inspection recipe basedon at least one of first information associated with the first image ofthe plastic shell or second information associated with the digitalfile. Block 706 may include performing operations of blocks 706, 708,and 710. At block 706, processing logic may apply the digital file tothe rules engine that uses the one or more rules. At block 708,processing logic may determine whether there are one or more features,one or more defects, and/or one or more possible defects included at oneor more locations in the plastic shell. For examples, processing logicmay determine whether there are one or more defects detected or one ormore possible defects (e.g., high risk areas for defects) detected basedon the first information associated with the first image of the plasticshell and/or on the second information associated with the digital file.The rules may specify that there are defects or possible defectsassociated with the features at certain locations. Further, the rulesmay specify capturing certain images if a defect is detected in theplastic shell. If there are no defects or possible defects included atthe one or more locations, the method 700 may conclude. If it isdetermined that there are one or more defects or possible defects at theone or more locations, then at block 710, processing logic may specifygenerating the one or more additional images of the plastic shellaccording to the one or more rules.

The rules may include rules associated with sets of features (e.g.,multiple features within a threshold proximity with one another) and/orwith individual parameters. Processing logic may determine the featuresof the plastic aligner based on the first information associated withthe first image or the second information associated with the digitalfile of the plastic aligner. The features may include at least one of anangle of a cutline at locations of the plastic aligner associated withan interproximal region of the dental arch of the patient, a curvatureof the plastic aligner, a thickness of the plastic aligner, an undercutheight associated with an attachment of a tooth of the tooth of thedental arch of the patient, a precision cutline, a distance betweencavities of the plastic aligner associated with attachments of teeth ofthe dental arch of the patient, and/or a number of the cavities of theplastic aligner. Any one or more of these features in combination may beindicative of a high risk area for a probable defect in the plasticaligner. Accordingly, processing logic may determine to generate one ormore additional images in the inspection recipe at the high risk areasfor the probable defects in the plastic aligner.

FIG. 8A illustrates a flow diagram for a method 800 of performing imagebased quality control for a shell, in accordance with one embodiment.One or more operations of method 800 are performed by processing logicof a computing device. The processing logic may include hardware (e.g.,circuitry, dedicated logic, programmable logic, microcode, etc.),software (e.g., instructions executed by a processing device), firmware,or a combination thereof. For example, one or more operations of method800 may be performed by a processing device executing an image basedquality control module 2150 of FIG. 21. It should be noted that themethod 800 may be performed for each unique aligner that is manufacturedfor each patient's treatment plan.

At block 802, processing logic may obtain one or more images of a firstshell (e.g., aligner). The first shell may have been manufactured for adental arch of a patient, as described above. The first shell may bereceived via an automated feed mechanism at an image based qualitycontrol (IBQC) station or may be placed in the IBQC station by a user.The IBQC station may include one or more cameras and a fixed or rotatingtable on which to position received shells. The IBQC station may alsoinclude a lighting system that is configured to provide uniform exposureand image capture with uniform ambient parameters. The processing logicmay configure the position of the cameras so one camera obtains top viewimages and another camera obtains side view images and/or diagonal viewimages of the shells in one embodiment. A rotating table may enableturning the shell being inspected in the IBQC station so images fromdifferent sides of the shell may be obtained.

The images that are obtained may include a first image that includes apart number identifier of the first shell. In some embodiments, an imageof the part number identifier with a light (e.g., white) background mayhelp with reading the laser markings that identify the part numberidentifier (e.g., barcode, serial number, or the like). Images of thealigner for quality control analysis may have a dark (e.g., black)background with the aligner evenly illuminated. The illumination of thealigner with a dark background may help distinguish the edges and shapeof the aligner for quality control analysis.

At block 804, a technician or processing logic may identify anidentifier (laser marking) on the first shell using the images. Forexample, in some embodiments, the processing logic may use opticalcharacter recognition for reading serial numbers or other text toidentifier the part number identifier of the imaged aligner. Optionally,a technician may identify and input a part number identifier by visuallyinspecting the aligner. The identifier may represent the part number andmay be laser marked on the aligner, as discussed above. The processinglogic may use the first image that has the light background to identifythe identifier. The identifier may be associated with a digital model ofthe aligner that is generated by the processing logic. In particular,prior to receiving images of the shell, the processing logic may receivea file including the digital model of the mold used to create theparticular aligner that is being inspected.

At block 806, processing logic may determine, from a set of digitalmodels of shells, a first digital model for the first shell based on theidentifier. Each digital model of the set of digital models is for aspecific shell customized for a specific patient at a particular stagein the patient's treatment plan. The digital models of the shells may begenerated based on digital models of the molds at each respective stageof the patient's treatment plan, as discussed in detail with referenceto method 1600 of FIG. 16.

At block 808, processing logic may compare the images of the first shellto projections of the first digital model. In one embodiment, processinglogic may compare a top view image of the first shell to a top view ofthe first digital model to determine whether a shape of the first shellis deformed. If one or more differences between a first shape of a firstprojection of the digital model of the shell and a second shape of thefirst shell exceeds a first threshold, then processing logic maydetermine that the shape of the first shell is deformed, as describedfurther below with reference to methods 900 and 1100 in FIG. 9A and FIG.11 and as illustrated by examples in FIGS. 10, 12, and 13A-13B.

However, if the one or more differences do not exceed the firstthreshold, then processing logic may perform additional comparisons. Forexample, processing logic may generate a modified projection of thedigital model by deforming the first shape of the first projection ofthe digital model of the first shell toward the second shape of thefirst shell to approximately match the second shape of the first shell.Processing logic may determine whether one or more remaining differencesbetween a third shape of the modified projection and the second shape ofthe first shell exceed a second threshold. If so, processing logic maydetermine that the cutline (or other property) of the first shell isdeformed, as described further below with reference to method 1400 inFIG. 14A and as illustrated by examples in FIGS. 15A-15C.

At block 810, processing logic may perform quality control for the firstshell based on the comparing. The results of the IBQC analysis may becompiled and a classification may be assigned to the aligner beinginspected. If any of the comparisons indicate that there is amanufacturing flaw present, then the processing logic may classify thealigner as defective. If every comparison does not indicate that a flawis present, then the processing logic may classify the aligner as notdefective. Optionally, the system may indicate that the aligner requiresfurther inspection by a technician when the analysis is inconclusive.The results may be presented to the user in a user interface.

FIG. 8B illustrates a flow diagram for another method 820 of performingimage based quality control for a shell, in accordance with oneembodiment. One or more operations of method 820 are performed byprocessing logic of a computing device. The processing logic may includehardware (e.g., circuitry, dedicated logic, programmable logic,microcode, etc.), software (e.g., instructions executed by a processingdevice), firmware, or a combination thereof. For example, one or moreoperations of method 820 may be performed by a processing deviceexecuting an image based quality control module 2150 of FIG. 21. Itshould be noted that the method 820 may be performed for each uniquealigner that is manufactured for each patient's treatment plan.

At block 822, processing logic may obtain one or more images of a firstshell (e.g., aligner). The first shell may have been manufactured for adental arch of a patient, as described above. The first shell may bereceived via an automated feed mechanism at an image based qualitycontrol (IBQC) station or may be placed in the IBQC station by a user.The IBQC station may include one or more cameras and a fixed or rotatingtable on which to position received shells. The IBQC station may alsoinclude a lighting system that is configured to provide uniform exposureand image capture with uniform ambient parameters. The processing logicmay configure the position of the cameras so one camera obtains top viewimages and another camera obtains side view images and/or diagonal viewimages of the shells in one embodiment. A rotating table may enableturning the shell being inspected in the IBQC station so images fromdifferent sides of the shell may be obtained.

The images that are obtained may include a first image that includes apart number identifier of the first shell. In some embodiments, an imageof the part number identifier with a light (e.g., white) background mayhelp with reading the laser markings that identify the part numberidentifier (e.g., barcode, serial number, or the like). Images of thealigner for quality control analysis may have a dark (e.g., black)background with the aligner evenly illuminated. The illumination of thealigner with a dark background may help distinguish the edges and shapeof the aligner for quality control analysis.

At block 824, a technician or processing logic may identify anidentifier (laser marking) on the first shell using the images. Forexample, in some embodiments, the processing logic may use opticalcharacter recognition for reading serial numbers or other text toidentify the part number identifier of the imaged aligner. Optionally, atechnician may identify and input a part number identifier by visuallyinspecting the aligner. The identifier may represent the part number andmay be laser marked on the aligner, as discussed above. The processinglogic may use the first image that has the light background to identifythe identifier. The identifier may be associated with a digital file.

At block 826, processing logic may determine, from a set of digitalfiles, a first digital file associated with the first shell based on theidentifier. Each digital file of the set of digital files includes adigital model of at least one of a shell (e.g., an aligner) or a digitalmodel of a mold used to manufacture the aligner. Each digital file isfor a specific shell customized for a specific patient at a particularstage in the patient's treatment plan.

In one embodiment, the digital file associated with the identifierincludes a digital model of the first shell (e.g., an aligner) that isdynamically generated by the processing logic or that is received fromanother source. The digital model of the first shell may be dynamicallygenerated by manipulating a digital model of a mold used to manufacturethe first shell. The digital model of the first shell may be generatedby simulating a process of thermoforming a film over a digital model ofthe mold by enlarging the digital model of the mold into an enlargeddigital model (e.g., by scaling or inflating a surface of the digitalmodel). Further, generation of the digital model of the first shell mayinclude computing a projection of a cutline onto the enlarged digitalmodel, virtually cutting the enlarged digital model along the cutline tocreate a cut enlarged digital model, and selecting the outer surface ofthe cut enlarged digital model. In one embodiment, the digital model ofthe first shell comprises an outer surface of the first shell, but doesnot necessarily have a thickness and/or does not comprise an innersurface of the first shell, though it may include a thickness or innersurface in other embodiments.

In one embodiment, the digital file includes a mold that is used tomanufacture the first shell. In one embodiment, the digital file mayinclude multiple files associated with the first shell, where themultiple files include a first digital file that comprises a digitalmodel of the mold and a second digital file comprises a digital model ofthe first shell. Alternatively, a single digital file may include both adigital model of the mold and a digital model of the first shell.

At block 828, processing logic determines an approximated first propertyfor the first shell from the first digital file. In one embodiment, theapproximated first property is based on a projection of the digitalmodel of the first shell onto a plane defined by an image of the firstshell. In one embodiment, the approximated first property is based on amanipulation of a digital model of a mold used to create the firstshell. For example, in some embodiments, the approximated first propertymay be based on a projection of the digital model of the mold onto theplane defined by the image of the first shell. In such instances, theprojection of the mold may be scaled or otherwise inflated toapproximate a projection of an aligner thermoformed on the mold. In afurther embodiment, the approximated first property is based on amanipulation of the digital model, wherein the manipulation causes anouter surface of the digital model to have an approximate shape of thefirst shell, and is further based on a projection of the outer surfaceof the digital model onto the surface defined by the image of the firstshell. In some embodiments, the approximated first property may includean approximated outer surface of the first shell. The approximated outersurface of the first shell may be referred to as a digital model of thefirst shell. In some embodiments, the approximated first property mayinclude a first shape of a projection of the approximated outer surfaceof the first shell onto a plane defined by an image of the first shell.

At block 830, processing logic determines a second property of the firstshell from the one or more images. An image of the first shell maydefine a plane. The second property may include a second shape of thefirst shell or projection thereof. The second property may be determineddirectly from the one or more images (e.g., top view, side view, etc.).In one embodiment, a contour of the second shape is drawn from theimage.

At block 832, processing logic may compare the approximated firstproperty to the second property. If one or more differences between theapproximated first property and the second property exceeds a firstthreshold, then processing logic may determine that the first shell isdeformed, as described further below with reference to methods 920 inFIG. 9B and as illustrated by the example in FIG. 10. For example, ifthe first shape fails to approximately match the second shape, then itmay be determined that the first shell is deformed.

However, if the one or more differences do not exceed the firstthreshold, then processing logic may perform additional comparisons. Forexample, processing logic may generate a modified projection of theapproximated outer surface of the first shell by deforming a curvatureof the first shape of the projection toward the second curvature of thesecond shape of the first shell to cause the curvature of a deformedfirst shape of the projection to approximately match the secondcurvature of the second shape of the first shell. Processing logic maydetermine whether one or more additional differences between thedeformed first shape and the second shape exceed a second threshold. Ifso, processing logic may determine that a cutline (or other property) ofthe first shell is inaccurate, as described further below with referenceto method 1420 and method 1440 in FIGS. 14B and 14C, respectively.

At block 834, processing logic may perform quality control for the firstshell based on the comparing. The results of the IBQC analysis may becompiled and a classification may be assigned to the shell beinginspected. If any of the comparisons indicate that there is amanufacturing flaw present, then the processing logic may classify theshell as defective. If every comparison does not indicate that a flaw ispresent, then the processing logic may classify the shell as notdefective. Optionally, the system may indicate that the shell warrantsfurther inspection by a technician when the analysis is inconclusive.The results may be presented to the user in a user interface.

FIG. 9A illustrates a flow diagram for a method 900 of determiningwhether a shape of the shell is deformed, in accordance with oneembodiment. One or more operations of method 900 are performed byprocessing logic of a computing device. The processing logic may includehardware (e.g., circuitry, dedicated logic, programmable logic,microcode, etc.), software (e.g., instructions executed by a processingdevice), firmware, or a combination thereof. For example, one or moreoperations of method 900 may be performed by a processing deviceexecuting an image based quality control module 2150 of FIG. 21. Method900 may be performed to determine whether shapes of the shells aredeformed. It should be noted that, in some embodiments, processing logicmay have performed block 802, 804, and 806 of method 800 (e.g., obtaineda top view image of the first shell and determined a first digital modelfor the first shell based on an identifier) prior to method 900executing.

At block 902, processing logic may determine a plane associated with thetop view image of the first shell. The top view image of the first shellmay include a two-dimensional object or a three-dimensional objectincluding pixels representing the image of the first shell lying in animage plane. At block 904, processing logic may project the firstdigital model of the first mold (or a manipulated digital model of amold used to manufacture the first shell) into the determined plane togenerate a first projection. For example, as depicted in FIG. 10, afirst projection 1000 of the digital model is projected onto an image1002 of the aligner. In some embodiments, the first projection 1000 isprojected into the same plane as the image 1002 of the first shell suchthat the first projection 1000 overlays the image 1002. Method 900 willbe discussed with reference to the first digital model of the firstaligner. However, it should be understood that the operations describedwork equally well using a manipulated digital model of a mold for thefirst shell.

At block 906, processing logic may identify, based on the comparingperformed at block 808 of method 800, one or more differences betweenthe first shape of the first projection 1000 and the second shape of thefirst shell. In some embodiments, processing logic may identify the oneor more differences by determining (block 908) one or more regions wherethe first shape of the first projection 1000 and the second shape of thefirst shell do not match. Processing logic may further determine (block910) the differences of the regions (e.g., at least one of a thicknessof the one or more regions or an area of the one or more regions).

At block 912, processing logic may determine whether the one or moredifferences (e.g., thickness, area, etc.) between the first shape of thefirst projection 1000 and the second shape of the first shell exceed thefirst threshold. The first threshold may be any suitable configurableamount (e.g., thickness greater than three millimeters (mm), 5 mm, 10mm, a region having an area greater than one hundred mm squared, etc.).At block 914, processing logic may determine whether the first shell isdeformed based on whether the one or more differences exceeds the firstthreshold. When a difference exceeds the first threshold, processinglogic may classify the aligner as deformed. When the one or moredifferences do not exceed the first threshold, processing logic maydetermine that the shape of the first shell is not deformed and mayproceed to perform additional quality control (e.g. cutline deformationdetection).

FIG. 9B illustrates a flow diagram for another method 920 of determiningwhether a shape of the shell is deformed, in accordance with oneembodiment. One or more operations of method 920 are performed byprocessing logic of a computing device. The processing logic may includehardware (e.g., circuitry, dedicated logic, programmable logic,microcode, etc.), software (e.g., instructions executed by a processingdevice), firmware, or a combination thereof. For example, one or moreoperations of method 920 may be performed by a processing deviceexecuting an image based quality control module 2150 of FIG. 21. Method920 may be performed to determine whether shapes of the shells aredeformed. It should be noted that, in some embodiments, processing logicmay have performed block 822, 824, 826, and 828 of method 820 (e.g.,obtained a top view image of the first shell, identify an identifier ofthe first shell, determined a first digital file for the first shellbased on an identifier, and determined an approximated first propertyfor the first shell from the first digital file) prior to method 920executing.

At block 922, processing logic may determine a plane associated with thetop view image of the first shell. The top view image of the first shellmay include pixels representing the image of the first shell lying in animage plane. At block 924, processing logic may compute a projection ofthe approximated outer surface of the first shell into the first plane.For example, as depicted in FIG. 10, a first projection 1000 of theapproximated outer surface of the first shell is projected onto an image1002 of the aligner. In some embodiments, the first projection 1000 isprojected into the same plane as the image 1002 of the first shell suchthat the first projection 1000 overlays the image 1002. In someembodiments, a digital model of a mold for the first shell ismanipulated by inflating or expanding a size of the digital model, wherethe amount of inflation or expansion is based on a thickness of thefirst shell. The inflated or expanded digital model of the mold may thenbe cut along a cut line to compute an approximated outer surface of thefirst shell. This approximated outer surface of the first shell may thenbe projected onto the plane at block 924.

At block 926, processing logic may identify, based on the comparingperformed at block 832 of method 820, one or more differences between afirst shape of the first projection 1000 and the second shape of thefirst shell. In some embodiments, processing logic may identify the oneor more differences by determining (block 928) one or more regions wherethe first shape of the first projection 1000 and the second shape of thefirst shell do not match. Processing logic may further determine (block930) the differences of the regions (e.g., at least one of a thicknessof the one or more regions or an area of the one or more regions).

At block 932, processing logic may determine whether the one or moredifferences (e.g., thickness, area, etc.) between the second shape ofthe first shell and the first shape of the first projection 1000 exceedthe first threshold. The first threshold may be any suitableconfigurable amount (e.g., thickness greater than three millimeters(mm), 5 mm, 10 mm, a region having an area greater than one hundred mmsquared, etc.). At block 934, processing logic may determine whether thefirst shell is deformed based on whether the one or more differencesexceeds the first threshold. When a difference exceeds the firstthreshold, processing logic may classify the aligner as deformed. Whenthe one or more differences do not exceed the first threshold,processing logic may determine that the shape of the first shell is notdeformed and may proceed to perform additional quality control (e.g.cutline deformation detection).

FIG. 11 illustrates a flow diagram for a method 1100 of determining adifference in shape between the digital model of the shell (e.g.,approximated outer surface of the shell without a thickness) and theimage of the shell, in accordance with one embodiment. One or moreoperations of method 1100 are performed by processing logic of acomputing device. The processing logic may include hardware (e.g.,circuitry, dedicated logic, programmable logic, microcode, etc.),software (e.g., instructions executed by a processing device), firmware,or a combination thereof. For example, one or more operations of method1100 may be performed by a processing device executing an image basedquality control module 2150 of FIG. 21.

In some embodiments, method 1100 may be performed to identify (block 906of method 900, block 926 of method 920) the one or more differencesbetween the shape of the first projection 1000 and the shape of thefirst shell. FIG. 12 illustrates an example user interface 1200depicting the first projection 1000 overlaid on a top view image 1202 ofthe first shell. Image based quality control may include pigmenting theobtained image of the aligner having the dark/black background such thatthe region occupied by the aligner is highlighted in a plane on theimage. The user interface 1200 may be implemented in computerinstructions stored on one or more memory devices and executable by oneor more processing devices of a computing device, such as the computingdevice 2100 in FIG. 21. The user interface 1200 may be displayed on adisplay of the computing device.

At block 1102, processing logic may generate a contour of the firstshell from the top view image 1202. As depicted, the first projection1000 has a first color (e.g., red) and the contour of the first shellhas a second color (e.g., blue). Processing logic may set (block 1104) aregion 1204 where the contour and the projection 1000 overlap to a thirdcolor (e.g., white). The first color, second color, and third color maydiffer from one another. Further, processing logic may determine the oneor more differences by identifying any region having the first color orthe second color. The portions of the projections that do not overlapmay be easily identified using the user interface 1200 based on thefirst and second colors bordering the third color of the region 1204.For example, the visible regions in FIG. 12 that are the first color(e.g., red) and the second color (e.g., blue) may be identified andcertain measurements may be taken, such as the thickness, area, and/orperimeter of the regions. If the measurements exceed the firstthreshold, then the processing logic may determine that the aligner isdeformed.

The user interface 1200 displays a popup message that provides variousoptions and results. The options may include connecting a camera,loading an image from the camera, loading an image from a file, andperforming the IBQC process. The results may include a classification(e.g., in this case, Deformed), and the actual results for an inwardparameter and outward parameter. The defect area for the inwardparameter is 96.9 and the defect thickness for the inward parameter is2.13. The defect area for the outward parameter is 60.9 and the defectthickness of the outward parameter is 1.85. Since one or more of thesemeasurements exceeds a desired threshold, the processing logic maydetermine that the aligner being inspected is deformed. A technician mayread this result and may have another aligner created, fix thedeformation, or the like.

FIGS. 13A-13B illustrate additional example comparisons of a contour ofa digital model of an aligner (e.g., approximated outer surface of thealigner without a thickness) with a contour of an image of the alignerto detect deformation, in accordance with one embodiment. FIG. 13Adepicts the top view image 1202 of the aligner with a first line 1300representing the contoured edge of the first projection 1000 of thedigital model of the aligner and a second line 1302 representing thecontoured edge of the image 1202 of the aligner. In some instances, thefirst line 1300 may be set to first color (e.g., red) and the secondline 1302 may be set to a second color (e.g., blue) different than thefirst color. A defect region for the aligner may be determined to bebetween the first line 1300 and the second line 1302. One or moremeasurements may be obtained from the defect region, as described above.For example, a thickness of the defect region, an area of the defectregion, etc. If the measurements exceed a threshold, then the alignermay be determined to be deformed.

FIG. 13B depicts a close-up visualization of a portion of the image1202. As depicted, the first line 1300 representing the contoured edgeof the projected digital model of the aligner does not match the secondline 1302 representing the contoured edge of the image 1202 of thealigner. As such, the aligner appears to be wider than expectedaccording to the first projection of the digital model of the aligner.In such a case, the processing logic may measure the thickness, area, orperimeter, of the region between the lines 1300 and 1302 and determinethat the aligner is deformed.

FIG. 14A illustrates a flow diagram for a method 1400 of deforming adigital model contour to more closely match the contour of the image ofthe aligner to detect other manufacturing defects (e.g., cutlinevariations, debris, webbing, trimmed attachments, and missingattachments, etc.), in accordance with one embodiment. One or moreoperations of method 1400 are performed by processing logic of acomputing device. The processing logic may include hardware (e.g.,circuitry, dedicated logic, programmable logic, microcode, etc.),software (e.g., instructions executed by a processing device), firmware,or a combination thereof. For example, one or more operations of method1400 may be performed by a processing device executing an image basedquality control module 2150 of FIG. 21. Method 1400 may includeoperations performed to detect other manufacturing defects of the shellsupon processing logic determining that differences of the shapes of thefirst projection and the image of the first shell are within the firstthreshold. While method 1400 may be described below as specificallydetecting cutline deviations, it should be understood that method 1400is equally applicable to detecting other manufacturing defects in thealigner.

For example, at block 1402, processing logic may determine that the oneor more differences (e.g., thickness, area, perimeter, etc.) do notexceed the first threshold. As a result, additional comparisons may beperformed by the processing logic to identify other deformations. Insome embodiments, at block 1404, processing logic may generate amodified projection of the first digital model of the aligner bydeforming a curvature of the first shape of the first projection 1000 tocause the curvature of the first shape to approximately match acurvature of the second shape of the first shell. The modifiedprojection may have a new third shape after deformation of thecurvature. To generate the modified projection, processing logic mayperform operations at blocks 1406-1414.

For purposes of clarity, FIGS. 15A-15C are discussed together withmethod 1400 because FIGS. 15A-15C illustrate examples of generating amodified projection by deforming a digital model contour to more closelymatch the contour of the image of the aligner to detect cutlinevariations, in accordance with one embodiment. FIG. 15A illustrates atop view of the contoured edges of an outline of the image of thealigner and the contoured edges of an outline of the first projection1000 of the digital model of the aligner. FIG. 15B illustrates thepoints on the middle line being pulled or shifted on the crossing linessuch that the entire outline of the digital model of the aligner isshifted outwards to more closely match the outline of the image of thealigner. FIG. 15C depicts the modified projection that has been deformedsuch that its curvature approximately matches the curvature of thecontour of the second shape of the shell.

At block 1406 of method 1400, processing logic may identify a middleline 1500 of the first projection 1000 of the digital model of thealigner, as depicted in FIG. 15A. In one instance, the middle line maybe approximated based on the thickness between the contoured edges ofthe digital model. Numerous pairs of points may be added to thecontoured edges of the outlines for both the image and the digital modelof the aligner. At block 1410, processing logic may project multiplecrossing lines 1502 that perpendicularly intersect the middle line 1500,also depicted in FIG. 15A. Pairs of points on the first projection 1000of the digital model and pairs of points on the image of the aligner maybe matched and the crossing lines 1502 that are perpendicular to themiddle line 1500 may intersect the matching pairs and the middle line1500. The middle line 1500 may be projected on the first projection1000. The crossing lines 1502 may also intersect at least two points onthe contour of the first shape of the first projection 1000 and at leasttwo points on the contour of the second shape of the shell. At block1412, processing logic may identify points on the crossing lines 1502 atan intersection between each respective line and the middle line 1500.

At block 1414, processing logic may move the points along the crossinglines 1502 to approximately match the contour of the first shape of thefirst projection 1000 with the contour of the second shape of the shell,as depicted in FIG. 15B. The processing logic may shift the points onthe middle line 1500 of the first projection 1000 of the digital modelto move the middle line 1500 of the digital model to a position where itlies in the middle of the image of the aligner. Once the processinglogic determines that the middle line 1500 of the first projection 1000of the digital model is as close to the middle of the image of thealigner, the processing logic may stop shifting the points. Moving thepoints may generate a modified projection 1504 that has the third shape.The third shape (e.g., outline of the modified projection 1504) may bemore closely matched to the outline of the image 1202 of the aligner, asdepicted in FIG. 15C.

When the outlines of the first projection 1000 of the digital model andthe image 1202 of the aligner are more closely matched, the processinglogic may then compare images of the cutline of the aligner with thecutline of the modified projection 1504 of the digital model of thealigner to identify (block 1416) remaining differences between thesecond shape of the first shell and the third shape of the modifiedprojection 1504. For example, processing logic may identify one or moreadditional regions where the second shape of the first shell and thethird shape of the modified projection 1504 do not match. The one ormore additional regions may correspond to a cutline of the first shell.At block 1418, processing logic may determine whether the remainingdifferences exceed a second threshold. For example, up to a 1 millimetervariation between the contours of the modified digital model and theimage may be tolerated. Any variation greater than or equal to 1millimeter may be determined to be a outline defect. It should be notedthat any suitable measurement threshold may be used. If the measuredregions exceed the second threshold, then the processing logic maydetermine that there is a outline deformation. That is, the processinglogic may determine that the outline of the first shell may interferewith a fit of the first shell on the dental arch of the patient. In someembodiments, side view images of the aligner may be used to compare toside views of the digital model when determining outline deformation.

FIG. 14B illustrates a flow diagram for another method 1420 of deformingan approximated outer surface (e.g., digital model of the alignerwithout a thickness) contour of the aligner to more closely match thecontour of the image of the aligner to detect outline variations, inaccordance with one embodiment. One or more operations of method 1420are performed by processing logic of a computing device. The processinglogic may include hardware (e.g., circuitry, dedicated logic,programmable logic, microcode, etc.), software (e.g., instructionsexecuted by a processing device), firmware, or a combination thereof.For example, one or more operations of method 1420 may be performed by aprocessing device executing an image based quality control module 2150of FIG. 21. Method 1420 may include operations performed to determineoutline deformation of the shells upon processing logic determining thatdifferences of the shapes of the first projection and the image of thefirst shell are within the first threshold.

For example, at block 1422, processing logic may determine that the oneor more differences (e.g., thickness, area, perimeter, etc.) between anapproximated property of the first shell and a measured property of thefirst shell do not exceed the first threshold. As a result, additionalcomparisons may be performed by the processing logic to identify otherdeformations. In some embodiments, at block 1424, processing logic maygenerate a modified projection of the approximated outer surface of thefirst shell by deforming the first shape of the first projection 1000 tocause a first curvature of a deformed first shape to approximately matcha second curvature of the second shape of the first shell. The modifiedprojection may have a new third shape after deformation. To generate themodified projection, processing logic may perform operations at blocks1426-1424.

For purposes of clarity, FIGS. 15A-15C are discussed together withmethod 1420 because FIGS. 15A-15C illustrate examples of generating amodified projection by deforming an approximated outer surface contourto more closely match the contour of the image of the aligner to detectoutline variations, in accordance with one embodiment. FIG. 15Aillustrates a top view of the contoured edges of an outline of the imageof the aligner and the contoured edges of an outline of the firstprojection 300 of the approximated outer surface of the aligner. FIG.15B illustrates the points on the middle line being pulled or shifted onthe crossing lines such that the entire outline of the approximatedouter surface of the aligner is shifted outwards to more closely matchthe outline of the image of the aligner. FIG. 15C depicts the modifiedprojection that has been deformed such that its shape approximatelymatches the contour of the shape of the shell.

At block 1426 of method 1420, processing logic may identify a middleline 1500 of the first projection 1000 of the approximated outer surfaceof the aligner, as depicted in FIG. 15A. In one instance, the middleline may be approximated based on the width between the contoured edgesof the approximated outer surface. Numerous pairs of points may be addedto the contoured edges of the outlines for both the image and theapproximated outer surface of the aligner. At block 1428, processinglogic may compute a projection of multiple crossing lines 1502 thatperpendicularly intersect the middle line 1500, also depicted in FIG.15A. Pairs of points on the first projection 1000 of the approximatedouter surface and pairs of points on the image of the aligner may bematched and the crossing lines 1502 that are perpendicular to the middleline 1500 may intersect the matching pairs and the middle line 1500. Themiddle line 1500 may be projected on the first projection 1000. Thecrossing lines 1502 may also intersect at least two points on thecontour of the first shape of the first projection 1000 and at least twopoints on the contour of the shape of the shell. At block 1430,processing logic may identify points on the crossing lines 1502 at anintersection between each respective line and the middle line 1500.

At block 1432, processing logic may move the points along the crossinglines 1502 to approximately match the contour of the first shape of thefirst projection 1000 with the contour of the second shape of the shell,as depicted in FIG. 15B. The processing logic may shift the points onthe middle line 1500 of the first projection 1000 of the approximatedouter surface of the first shell to move the middle line 1500 of theapproximated outer surface to a position where it lies in the middle ofthe image of the aligner. Once the processing logic determines that themiddle line 1500 of the first projection 1000 of the approximated outersurface is as close to the middle of the image of the aligner, theprocessing logic may stop shifting the points. Moving the points maygenerate a modified projection 1504 that has a deformed first shapeincluding a curvature that approximately matches the curvature of thesecond shape.

When the outlines of the first projection 1000 of the approximated outersurface and the image 1202 of the aligner are more closely matched, theprocessing logic may then compare images of the cutline of the alignerwith the cutline of the modified projection 1504 of the approximatedouter surface of the aligner to identify (block 1434) additionaldifferences between the first curvature of the deformed second shape andthe second curvature of the first shape of the first shell. For example,processing logic may identify one or more additional regions where thesecond curvature and the first curvature do not match. Such a comparisonmay be performed based on the remaining differences between the thirdshape of the modified projection and the second shape of the first shellas measured from the top view image. Alternatively, or additionally, anew projection may be computed onto a second plane defined by a sideview image based on the modified projection in the first plane definedby the top view image. The one or more additional regions may correspondto a cutline of the first shell. At block 1436, processing logic maydetermine whether the additional differences exceed a second threshold.For example, up to a 1 millimeter variation between the contours of themodified approximated outer surface and the image may be tolerated. Anyvariation greater than or equal to 1 millimeter may be determined to bea cutline defect. It should be noted that any suitable measurementthreshold may be used. If the measured regions exceed the secondthreshold, then the processing logic may determine that there is acutline deformation. That is, the processing logic may determine thatthe cutline of the first shell may interfere with a fit of the firstshell on the dental arch of the patient.

FIG. 14C illustrates a flow diagram for another method 1440 of deformingan approximated outer surface (e.g., digital model of the alignerwithout a thickness) contour of the aligner to more closely match thecontour of the image of the aligner to detect cutline variations, inaccordance with one embodiment. One or more operations of method 1440are performed by processing logic of a computing device. The processinglogic may include hardware (e.g., circuitry, dedicated logic,programmable logic, microcode, etc.), software (e.g., instructionsexecuted by a processing device), firmware, or a combination thereof.For example, one or more operations of method 1440 may be performed by aprocessing device executing an image based quality control module 21 ofFIG. 21. Method 1440 may include operations performed to determinecutline deformation of the shells upon processing logic determining thatdifferences of the shapes of the first projection and the image of thefirst shell are within the first threshold. The method 1440 may use aside view image of the first shell.

At block 1442, processing logic may generate a modified projection ofthe approximated outer surface of the first shell by deforming the firstshape of the first projection to cause a first curvature of a deformedfirst shape to approximately match a second curvature of the secondshape of the first shell. The deforming of the shape of the firstprojection may be performed as described above.

At block 1444, processing logic may determine a second plane associatedwith the side view image. At block 1446, processing logic may deform theapproximated outer surface of the first shell in accordance with thedeforming of the second shape of the first projection. At block 1448,processing logic may compute a second projection of the deformedapproximated outer surface of the first shell onto the second plane. Atblock 1450, processing logic may determine additional differencesbetween a third shape of the first shell as represented in the side viewimage and an approximated fourth shape of the first shell as representedin the second projection.

At block 1452, processing logic may determine whether the one or moreadditional differences exceed a second threshold. For example, up to a 1millimeter variation between the contours of the second projection ofthe deformed approximated outer surface and the second image may betolerated. Any variation greater than or equal to 1 millimeter may bedetermined to be a outline defect. It should be noted that any suitablemeasurement threshold may be used. If the measured regions exceed thesecond threshold, then the processing logic may determine that there isa outline deformation. That is, the processing logic may determine thatthe outline of the first shell may interfere with a fit of the firstshell on the dental arch of the patient.

FIG. 16 illustrates a flow diagram for a method 1600 of generating adigital model of the shell (e.g., approximated outer surface of thealigner) that may be included in a digital file, in accordance with oneembodiment. Alternatively, or additionally, method 1600 may be performedusing a digital model of a mold for a shell to approximate a property ofthe shell that can be compared to a measured property of the shell thatis determined from an image of the shell. Accordingly, method 1600 maybe performed without generating a digital model of the shell. One ormore operations of method 1600 are performed by processing logic of acomputing device. The processing logic may include hardware (e.g.,circuitry, dedicated logic, programmable logic, microcode, etc.),software (e.g., instructions executed by a processing device), firmware,or a combination thereof. For example, one or more operations of method1600 may be performed by a processing device executing an image basedquality control module 2150 of FIG. 21. Each digital model of aparticular shell at a particular stage in a patient's treatment plan maybe generated based on manipulating a digital model of a mold of thepatient's dental arch at that particular stage in the patient'streatment plan. The mold is manufactured using the digital model of themold (e.g., via 3D printing) and the shells are manufactured using athermoforming process with the mold. The digital models of the shellsmay not be used to manufacture the shells.

At block 1602, processing logic may simulate a process of thermoforminga film over the digital model of the mold by enlarging the digital modelof the mold into an enlarged digital model. The enlarging performed mayaccount for the thickness of the shell. The inflation or enlarging mayscale the surface of the digital model of the mold by a predeterminedfactor. Processing logic may determine a outline for the enlargeddigital model of the shell. In one instance, the processing logic maydetermine the outline for a surface of the enlarged digital model of theshell by finding an intersecting line between gingival and teeth andmodifying the intersecting line by raising it so as not to touch thegingiva. At block 1604, processing logic may project a outline onto theenlarged digital model. In some embodiments, the outline may bedisplayed to a user as a line having a certain color (e.g., yellow)superimposed on the enlarged digital model of the surface of thealigner.

At block 1606, processing logic may virtually cut the enlarged digitalmodel along the outline to create a cut enlarged digital model. That is,the determined outline may be used during the simulated process toremove the excess enlarged surface to generate the virtual surface ofthe aligner by cutting along the determined virtual outline. At block1608, processing logic may select an outer surface of the cut enlargeddigital model as the digital model of the shell. The digital model ofthe shell may result from the simulated trimming and the digital modelmay represent an outer surface of the aligner. The digital model of thealigner may be three-dimensional and various two-dimensional views(e.g., top view, side view) or three-dimensional views may be obtainedusing the digital model of the aligner. Further, the digital model ofthe aligner may be associated with the part number identifier for therespective aligner such that the digital model of the aligner may beretrieved when the processing logic identifies the part numberidentifier using an image of the manufactured aligner. In alternativeembodiments, a technician may read the part number identifier and inputit into the IBQC system. Alternatively, other computer readable tagsassociated with the aligner may be read by the IBQC system for partnumber identification. The system may receive the part number identifierinformation, then retrieve from a database a previously generateddigital model of the aligner or the digital model of the mold associatedwith the aligner for generation of a digital model of the aligner.Accordingly, a separate digital model of an aligner may be generated foreach aligner at each stage in a patient's treatment plan prior to orduring the quality control process. The processing logic may use theidentified part number identifier to perform the IBQC for each uniquelymanufactured aligner.

FIG. 17 illustrates a flow diagram for a general method 1700 ofdetermining a resting position of the digital model of the shell on aflat surface, in accordance with one embodiment. The digital model maybe included in the digital file and the digital model may include theapproximated first property (e.g., outer surface of the shell). Thedigital model of the shell may be generated based on the manipulation ofthe digital model of the mold. One or more operations of method 1700 areperformed by processing logic of a computing device. The processinglogic may include hardware (e.g., circuitry, dedicated logic,programmable logic, microcode, etc.), software (e.g., instructionsexecuted by a processing device), firmware, or a combination thereof.For example, one or more operations of method 1700 may be performed by aprocessing device executing an image based quality control module 2150of FIG. 21. Method 1700 may be performed to project the first projectionof the digital model of the shell into the same plane associated withthe top view image of the shell. It may also be used when projecting thedigital model of the shell into other planes associated with images ofthe shell taken from other angles. Method 1700 may be performed forexample, to correctly compute a projection of the digital model into theplane associated with a top view image of an aligner in block 904 and/or924 of method 900 and/or 920, respectively.

At block 1702, processing logic may determine a resting position of thefirst digital model for the first shell on a flat surface. Method 1800of FIG. 18 describes determining the resting position of the digitalmodel of the shell on the flat surface using a two-dimensional digitalmodel, and FIGS. 19A-19C depict examples for determining the restingposition using the two-dimensional digital model. Method 2000 of FIG. 20describes determining the resting position of the digital model of theshell on the flat surface using a three-dimensional digital model. Oncethe resting position is determined, processing logic may compute aprojection (block 1704) of the first digital model having the restingposition onto the plane of the top view image.

FIG. 18 illustrates a flow diagram for a method 1800 of determining aresting position of the digital model of the shell (e.g., approximatedouter surface of the shell) on a flat surface using a two-dimensionaldigital model, in accordance with one embodiment. One or more operationsof method 1800 are performed by processing logic of a computing device.The processing logic may include hardware (e.g., circuitry, dedicatedlogic, programmable logic, microcode, etc.), software (e.g.,instructions executed by a processing device), firmware, or acombination thereof. For example, one or more operations of method 1800may be performed by a processing device executing an image based qualitycontrol module 2150 of FIG. 21. For purposes of clarity, method 1800 andFIGS. 19A-19C are discussed together below.

FIG. 19A illustrates a two-dimensional contour of an arbitrary object1900. Although the arbitrary object 1900 is depicted, it should beunderstood that the method 1800 may be applied to an outline of adigital model of a shell. At block 1802, processing logic may determinea center of mass 1902 of the object 1900. At block 1804, processinglogic may determine a convex hull 1904 (e.g., polygon) of the object1900. The convex hull may include numerous vertices that link the outermost points of the object 1900. In particular, three vertices F1, F2,and F3 are identified on the convex hull 1904.

For each vertex of the convex hull 1904, processing logic may compute(block 1806) a line 1906 containing that vertex. At block 1808,processing logic may compute a projection of the center of mass 1902onto this line 1906, as shown by projected point 1908 in FIG. 19B. Atblock 1810, processing logic may determine whether the projected point1908 lies outside the vertex. For vertex F1, the projected point 1908 isoutside of the vertex F1, so the object 1900 may not rest on side F1because the object 1900 will roll to the left. The same is true forvertex F2. If the projected point 1908 lies outside the vertex, then theprocessing logic may return to repeat blocks 1806, 1808, and 1810 untilthe resting position is found.

If the projected point 1908 lies within the vertex, then processinglogic may determine that the particular vertex is the resting positionfor the first digital model of the first shell. FIG. 19C illustratesdrawing a line 1910 containing vertex F3. The center of mass 1902 isprojected on this line 1910, and as depicted, the projected point 1912lies within vertex F3 that defines the line 1910. As such, the object1900 can rest on this side without rolling. Using such a method, theprocess logic may determine a resting position and identify theappropriate projection of the digital model of the aligner for qualitycontrol analysis depending on the image view (e.g., top view, side view,etc.).

FIG. 20 illustrates a flow diagram for a method 2000 of determining aresting position of the digital model of the shell on a flat surfaceusing a three-dimensional digital model, in accordance with oneembodiment. One or more operations of method 2000 are performed byprocessing logic of a computing device. The processing logic may includehardware (e.g., circuitry, dedicated logic, programmable logic,microcode, etc.), software (e.g., instructions executed by a processingdevice), firmware, or a combination thereof. For example, one or moreoperations of method 2000 may be performed by a processing deviceexecuting an image based quality control module 2150 of FIG. 21.

At block 2002, processing logic may determine a center of mass of thefirst digital model of the first shell. At block 2004, processing logicmay determine a convex hull (e.g., polyhedron for the three-dimensionaldigital model) of the digital model. The convex hull may includenumerous faces that link the outer most points of the first digitalmodel. For each face of the convex hull, processing logic may compute(block 2006) a plane containing that face. At block 2008, processinglogic may compute a projection of the center of mass onto this plane. Atblock 2010, processing logic may determine whether the projected pointon the plane lies outside the face. If the projected point lays outsidethe face, then the processing logic may return to repeat blocks 2006,2008, and 2010 until the resting position is found. If the projectedpoint does not lie outside the face, processing logic may determine(block 2012) that the face is the resting position for the first digitalmodel.

FIG. 21 illustrates a diagrammatic representation of a machine in theexample form of a computing device 2100 within which a set ofinstructions, for causing the machine to perform any one or more of themethodologies discussed herein. In some embodiments, the machine may bepart of an IBQC station or communicatively coupled to the IBQC station.In alternative embodiments, the machine may be connected (e.g.,networked) to other machines in a Local Area Network (LAN), an intranet,an extranet, or the Internet. For example, the machine may be networkedto the IBQC station and/or a rapid prototyping apparatus such as a 3Dprinter or SLA apparatus. The machine may operate in the capacity of aserver or a client machine in a client-server network environment, or asa peer machine in a peer-to-peer (or distributed) network environment.The machine may be a personal computer (PC), a tablet computer, aset-top box (STB), a Personal Digital Assistant (PDA), a cellulartelephone, a web appliance, a server, a network router, switch orbridge, or any machine capable of executing a set of instructions(sequential or otherwise) that specify actions to be taken by thatmachine. Further, while only a single machine is illustrated, the term“machine” shall also be taken to include any collection of machines(e.g., computers) that individually or jointly execute a set (ormultiple sets) of instructions to perform any one or more of themethodologies discussed herein.

The example computing device 2100 includes a processing device 2102, amain memory 2104 (e.g., read-only memory (ROM), flash memory, dynamicrandom access memory (DRAM) such as synchronous DRAM (SDRAM), etc.), astatic memory 2106 (e.g., flash memory, static random access memory(SRAM), etc.), and a secondary memory (e.g., a data storage device2128), which communicate with each other via a bus 2108.

Processing device 2102 represents one or more general-purpose processorssuch as a microprocessor, central processing unit, or the like. Moreparticularly, the processing device 2102 may be a complex instructionset computing (CISC) microprocessor, reduced instruction set computing(RISC) microprocessor, very long instruction word (VLIW) microprocessor,processor implementing other instruction sets, or processorsimplementing a combination of instruction sets. Processing device 2102may also be one or more special-purpose processing devices such as anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), a digital signal processor (DSP), network processor,or the like. Processing device 2102 is configured to execute theprocessing logic (instructions 2126) for performing operations and stepsdiscussed herein.

The computing device 2100 may further include a network interface device2122 for communicating with a network 2164. The computing device 2100also may include a video display unit 2110 (e.g., a liquid crystaldisplay (LCD) or a cathode ray tube (CRT)), an alphanumeric input device2112 (e.g., a keyboard), a cursor control device 2114 (e.g., a mouse),and a signal generation device 2120 (e.g., a speaker).

The data storage device 2128 may include a machine-readable storagemedium (or more specifically a non-transitory computer-readable storagemedium) 2124 on which is stored one or more sets of instructions 2126embodying any one or more of the methodologies or functions describedherein. A non-transitory storage medium refers to a storage medium otherthan a carrier wave. The instructions 2126 may also reside, completelyor at least partially, within the main memory 2104 and/or within theprocessing device 2102 during execution thereof by the computer device2100, the main memory 2104 and the processing device 2102 alsoconstituting computer-readable storage media.

The computer-readable storage medium 2124 may also be used to store oneor more virtual 3D models (also referred to as electronic models) and/oran IBQC module 2150, which may perform one or more of the operations ofthe methods described herein. The computer readable storage medium 2124may also store a software library containing methods that call an IBQCmodule 2150. While the computer-readable storage medium 2124 is shown inan example embodiment to be a single medium, the term “computer-readablestorage medium” should be taken to include a single medium or multiplemedia (e.g., a centralized or distributed database, and/or associatedcaches and servers) that store the one or more sets of instructions. Theterm “computer-readable storage medium” shall also be taken to includeany medium that is capable of storing or encoding a set of instructionsfor execution by the machine and that cause the machine to perform anyone or more of the methodologies of the present invention. The term“computer-readable storage medium” shall accordingly be taken toinclude, but not be limited to, solid-state memories, and optical andmagnetic media.

It is to be understood that the above description is intended to beillustrative, and not restrictive. Many other embodiments will beapparent upon reading and understanding the above description. Althoughembodiments of the present invention have been described with referenceto specific example embodiments, it will be recognized that theinvention is not limited to the embodiments described, but can bepracticed with modification and alteration within the spirit and scopeof the appended claims. Accordingly, the specification and drawings areto be regarded in an illustrative sense rather than a restrictive sense.The scope of the invention should, therefore, be determined withreference to the appended claims, along with the full scope ofequivalents to which such claims are entitled.

What is claimed is:
 1. A method of manufacturing an orthodontic aligner,comprising: manufacturing the orthodontic aligner, wherein manufacturingthe orthodontic aligner comprises: printing a mold associated with adental arch of a patient based on a digital model of the mold; formingthe orthodontic aligner over the mold; and trimming the orthodonticaligner, wherein after the trimming the orthodontic aligner comprises acutline; and assessing a quality of the orthodontic aligner, whereinassessing the quality of the orthodontic aligner comprises: imaging theorthodontic aligner to generate a first digital representation of theorthodontic aligner; comparing, by a processor, the first digitalrepresentation of the orthodontic aligner to a digital file associatedwith the orthodontic aligner; determining, based on the comparing,whether a cutline variation is detected between the orthodontic alignerand the digital file; and determining whether there is a manufacturingdefect of the orthodontic aligner based on whether the cutline variationexceeds a cutline variation threshold.
 2. The method of claim 1, whereinassessing the quality of the orthodontic aligner further comprises:determining, based on the comparing, whether an arch variation isdetected between the orthodontic aligner and the digital file; anddetermining whether there is a manufacturing defect of the orthodonticaligner based on whether the arch variation exceeds an arch variationthreshold.
 3. The method of claim 1, wherein assessing the quality ofthe orthodontic aligner further comprises: determining, based on thecomparing, whether a bend of the orthodontic aligner is detected betweenthe orthodontic aligner and the digital file; and determining whetherthere is a manufacturing defect of the orthodontic aligner based onwhether the bend exceeds a bend threshold.
 4. The method of claim 1,wherein the digital file comprises a second representation of theorthodontic aligner with attachments, and wherein assessing the qualityof the orthodontic aligner further comprises: determining, based on thecomparing, whether one or more of the attachments are trimmed or missingin the first digital representation of the orthodontic aligner; anddetermining whether there is a manufacturing defect of the orthodonticaligner based on whether one or more of the attachments are trimmed ormissing in the first digital representation of the orthodontic aligner.5. The method of claim 1, wherein the cutline variation is a gingivalcutline variation that represents an interface between the orthodonticaligner and a gingiva of the patient.
 6. The method of claim 1, whereinthe digital file comprises trimming instructions for trimming theorthodontic aligner along the cutline.
 7. The method of claim 1, whereincomparing the first digital representation of the orthodontic aligner tothe digital file associated with the orthodontic aligner comprisescomparing an edge of the first digital representation of the orthodonticaligner to a virtual cutline included in the digital file.
 8. The methodof claim 7, wherein assessing the quality of the orthodontic alignerfurther comprises: determining, based on the comparing, whether at leasta portion of the virtual cutline corresponds to the edge of the of thefirst digital representation of the orthodontic aligner.
 9. The methodof claim 1, wherein comparing the first digital representation of theorthodontic aligner to the digital file associated with the orthodonticaligner comprises overlaying a virtual cutline from the digital filewith the first digital representation of the orthodontic aligner. 10.The method of claim 1, wherein the first digital representation of theorthodontic aligner comprises a two-dimensional image of the orthodonticaligner.
 11. The method of claim 1, wherein the first digitalrepresentation of the orthodontic aligner comprises a three-dimensionalmodel of the orthodontic aligner.
 12. The method of claim 1, furthercomprising: enhancing one or more surface features of the orthodonticaligner while imaging the orthodontic aligner.
 13. The method of claim1, further comprising: classifying the orthodontic aligner as defectiveresponsive to determining that the orthodontic aligner has amanufacturing defect.
 14. The method of claim 13, wherein classifyingthe orthodontic aligner as defective causes a replacement of theorthodontic aligner to be manufactured.
 15. The method of claim 13,wherein classifying the orthodontic aligner as defective indicates atleast one of a) that the orthodontic aligner requires further inspectionby a technician or b) that the orthodontic aligner is rejected.
 16. Themethod of claim 1, wherein the first digital representation has a firstcurvature, wherein the digital file comprises a second digitalrepresentation of the orthodontic aligner having a second curvature, andwherein assessing the quality of the orthodontic aligner furthercomprises: modifying the digital file to cause the second digitalrepresentation to approximately have the first curvature beforedetermining whether the cutline variation is detected between theorthodontic aligner and the digital file.
 17. A method of manufacturingan orthodontic aligner, comprising: manufacturing the orthodonticaligner, wherein manufacturing the orthodontic aligner comprises:printing a mold associated with a dental arch of a patient based on adigital model of the mold; forming the orthodontic aligner over themold; and trimming the orthodontic aligner; and assessing a fit of theorthodontic aligner on the dental arch of the patient, wherein assessingthe fit of the orthodontic aligner on the dental arch of the patientcomprises: receiving a first digital representation of the orthodonticaligner, the first digital representation having been generated based onimaging of the orthodontic aligner; analyzing the first digitalrepresentation of the orthodontic aligner to identify a quality-relatedproperty of the orthodontic aligner, wherein the quality-relatedproperty is identified based at least in part on whether a cutlinevariation between the first digital representation of the orthodonticaligner and a digital file associated with the orthodontic alignerexceeds a cutline variation threshold; and determining, based on thequality-related property of the orthodontic aligner, a fit of theorthodontic aligner on the dental arch of the patient.
 18. The method ofclaim 17, further comprising: classifying the orthodontic aligner asdefective based on determining that the quality-related property of theorthodontic aligner interferes with the fit of the orthodontic aligneron the dental arch of the patient.
 19. The method of claim 18, whereinclassifying the orthodontic aligner as defective causes a replacement ofthe orthodontic aligner to be manufactured.
 20. The method of claim 18,wherein classifying the orthodontic aligner as defective indicates atleast one of a) that the orthodontic aligner requires further inspectionby a technician or b) that the orthodontic aligner is rejected.
 21. Themethod of claim 17, wherein the orthodontic aligner is associated with astage of treatment of the patient, and wherein analyzing the firstdigital representation of the orthodontic aligner comprises performing acomparison of the first digital representation of the orthodonticaligner with the digital file that is associated with the patient'sdental arch at the stage of treatment of the patient.
 22. The method ofclaim 21, wherein the digital file comprises trimming instructions usedto trim the orthodontic aligner along a cutline, the method furthercomprising: determining, based on the comparison, whether theorthodontic aligner was trimmed in a manner that will interfere with thefit of the orthodontic aligner on the dental arch.
 23. The method ofclaim 21, wherein analyzing the first digital representation of theorthodontic aligner further comprises performing a comparison of thefirst digital representation of the orthodontic aligner to a seconddigital representation of at least one of the orthodontic aligner or thedental arch of the patient, the method further comprising: determining,based on the comparison, whether the orthodontic aligner comprises adeformation that will interfere with the fit of the orthodontic aligneron the dental arch.
 24. The method of claim 23, wherein the seconddigital representation of at least one of the orthodontic aligner or thedental arch of the patient comprises a three dimensional model of atleast one of the orthodontic aligner or the dental arch of the patient.25. The method of claim 21, wherein the digital file or an additionaldigital file comprises a representation of the patient's dental arch atthe stage of treatment or a representation of a target version of theorthodontic aligner at the stage of treatment, and wherein thequality-related property of the orthodontic aligner comprises at leastone of the cutline variation, an arch variation, or a bend between theorthodontic aligner and the representation of the patient's dental archat the stage of treatment or the representation of the target version ofthe orthodontic aligner at the stage of treatment.
 26. The method ofclaim 17, further comprising: determining, based on the quality-relatedproperty of the orthodontic aligner, whether the quality-relatedproperty will interfere with an efficacy of the orthodontic aligner foraligning one or more teeth of the patient.
 27. The method of claim 17,wherein the first digital representation of the orthodontic alignerincludes one or more two-dimensional images corresponding to one or moreviews of the orthodontic aligner.
 28. The method of claim 17, furthercomprising: comparing an image from among the one or moretwo-dimensional images of the orthodontic aligner with a projection of avirtual three-dimensional (3D) model associated with the orthodonticaligner.
 29. The method of claim 17, further comprising: imaging theorthodontic aligner to generate the first digital representation of theorthodontic aligner; and enhancing surface features of the orthodonticaligner via illumination during the imaging to facilitate capture of thesurface features.
 30. The method of claim 17, further comprising:flagging the orthodontic aligner for further inspection.